To classify survey comments into predefined topics, especially when allowing up to three classes per comment, you can follow these steps:

Data Preparation:
Collect and Clean Data: Gather all survey comments and clean the text by removing any irrelevant information (e.g., special characters, stop words).
Label Data: If you have a labeled dataset, use it to train your model. If not, you might need to manually label a subset of comments to create a training set.
Feature Extraction:
Text Vectorization: Convert text data into numerical format using techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings (e.g., Word2Vec, GloVe, BERT).
Model Selection:
Choose a Classification Algorithm: Use machine learning models such as Logistic Regression, Support Vector Machines (SVM), or more advanced models like BERT for text classification.
Multi-label Classification: Since each comment can belong to multiple topics, use multi-label classification techniques. Algorithms like Binary Relevance, Classifier Chains, or neural networks designed for multi-label tasks can be effective.
Training the Model:
Split Data: Divide your data into training and testing sets.
Train the Model: Use your training data to train the chosen model. Ensure you use appropriate evaluation metrics like F1-score, Precision, and Recall for multi-label classification.
Classification:
Predict Topics: For each new survey comment, use the trained model to predict the relevant topics. Ensure the model can assign up to three topics per comment.
Post-processing:
Thresholding: Apply a threshold to the model's output probabilities to decide which topics to assign to each comment.
Evaluation: Continuously evaluate the model's performance on new data and adjust as necessary.
Automation and Integration:
Automate the Process: Integrate the model into your survey analysis pipeline to automatically classify new comments.
Feedback Loop: Use feedback from the classified comments to retrain and improve the model over time.

By following these steps, you can effectively classify survey comments into predefined topics, allowing for a more nuanced analysis of employee experience. If you need more detailed guidance on any specific step, feel free to ask!

Here's an example of how you can use Python with the scikit-learn library to classify survey comments into predefined topics, allowing up to three topics per comment. This example uses a multi-label classification approach with a OneVsRestClassifier and LogisticRegression.

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

# Sample data
data = {
    'comment': [
        "I love the flexible work hours.",
        "The management is supportive.",
        "Great work-life balance.",
        "Need better communication from the top.",
        "More opportunities for growth would be nice."
    ],
    'topics': [
        ["Work Hours", "Work-Life Balance"],
        ["Management"],
        ["Work-Life Balance"],
        ["Communication"],
        ["Career Growth"]
    ]
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Binarize the labels
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
y = mlb.fit_transform(df['topics'])

# Vectorize the comments
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(df['comment'])

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = OneVsRestClassifier(LogisticRegression())
model.fit(X_train, y_train)

# Predict on the test set
y_pred = model.predict(X_test)

# Evaluate the model
print(classification_report(y_test, y_pred, target_names=mlb.classes_))

# Example prediction
new_comments = ["I appreciate the career development programs."]
X_new = vectorizer.transform(new_comments)
predictions = model.predict(X_new)
predicted_topics = mlb.inverse_transform(predictions)
print(predicted_topics)

This code does the following: 1. Data Preparation: Creates a sample dataset with comments and their associated topics. 2. Label Binarization: Converts the list of topics into a binary format suitable for multi-label classification. 3. Text Vectorization: Uses TF-IDF to convert text comments into numerical features. 4. Model Training: Trains a OneVsRestClassifier with LogisticRegression on the training data. 5. Evaluation: Evaluates the model's performance on the test set and prints a classification report. 6. Prediction: Demonstrates how to predict topics for new comments.

You can expand this code to include your full dataset and adjust the model parameters as needed. If you have any specific requirements or need further customization, let me know!

To test if the same topic structure works on another kind of survey and to include an "Other" topic for themes not covered, you can follow these steps:

Data Collection and Preparation:
Collect New Survey Data: Gather responses from the new survey.
Clean and Preprocess: Clean the text data similarly to how you did for the original survey.
Feature Extraction:
Vectorize New Survey Comments: Use the same vectorization method (e.g., TF-IDF) and the same vectorizer instance to transform the new survey comments into numerical features.
Model Application:
Load Pre-trained Model: Use the model trained on the original survey data.
Predict Topics: Apply the model to the new survey data to predict topics.
Evaluation:
Manual Review: Manually review a sample of the predictions to see if the topics make sense in the context of the new survey.
Quantitative Metrics: If you have labeled data for the new survey, calculate metrics like Precision, Recall, and F1-score to evaluate the model's performance.
Incorporate "Other" Topic:
Thresholding: Set a probability threshold for topic assignment. If no topic exceeds the threshold, classify the comment as "Other".
Manual Labeling: Manually review comments classified as "Other" to identify any new themes that might need to be added to the predefined topics.

Here's an example of how you can modify the code to include an "Other" topic:

import numpy as np

# Function to predict topics with an "Other" category
def predict_with_other(model, vectorizer, mlb, comments, threshold=0.2):
    X_new = vectorizer.transform(comments)
    probabilities = model.predict_proba(X_new)
    predictions = (probabilities >= threshold).astype(int)

    # If no topic exceeds the threshold, classify as "Other"
    for i in range(predictions.shape[0]):
        if not np.any(predictions[i]):
            predictions[i] = np.zeros(predictions.shape[1])
            predictions[i, -1] = 1  # Assuming the last column is "Other"

    return mlb.inverse_transform(predictions)

# Example usage
new_comments = ["I appreciate the career development programs.", "The cafeteria food needs improvement."]
predicted_topics = predict_with_other(model, vectorizer, mlb, new_comments)
print(predicted_topics)

In this code: 1. Thresholding: The predict_with_other function applies a threshold to the predicted probabilities. If no topic exceeds the threshold, the comment is classified as "Other". 2. Manual Review: You can manually review comments classified as "Other" to identify any new themes.

By following these steps, you can test the applicability of your topic structure on a new survey and ensure that any new themes are captured effectively. If you need further customization or have specific requirements, feel free to ask!

Here's a Python code snippet that classifies survey comments into predefined topics, including "No Topic" and "Other Topic," and assigns up to 3 classes per survey comment:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# Predefined topics
topics = [
    "Work Environment",
    "Compensation",
    "Management",
    "Work-Life Balance",
    "Career Growth",
    "Company Culture",
    "Training and Development",
    "Job Security",
    "Benefits",
    "Communication",
    "No Topic",
    "Other Topic"
]

# Sample survey comments
survey_comments = [
    "I am happy with the work environment and the management is supportive.",
    "The compensation is not competitive and there are no benefits.",
    "I feel there is no work-life balance and the company culture is toxic.",
    "There are no opportunities for career growth and job security is a concern.",
    "The training and development programs are excellent.",
]

# Vectorize the topics and survey comments
vectorizer = TfidfVectorizer()
topic_vectors = vectorizer.fit_transform(topics)
comment_vectors = vectorizer.transform(survey_comments)

# Function to classify survey comments to topics
def classify_comments(comments, topic_vectors, n_classes=3):
    classifications = []

    for comment_vector in comments:
        # Calculate cosine similarity between comment and topics
        similarities = cosine_similarity(comment_vector, topic_vectors).flatten()

        # Get indices of top n_classes topics
        top_indices = similarities.argsort()[-n_classes:][::-1]

        # Get the corresponding topics
        top_topics = [topics[i] for i in top_indices]

        classifications.append(top_topics)

    return classifications

# Classify the survey comments
classified_comments = classify_comments(comment_vectors, topic_vectors)

# Print the results
for i, comment in enumerate(survey_comments):
    print(f"Survey Comment: {comment}")
    print(f"Classified Topics: {classified_comments[i]}")
    print()

This code uses TF-IDF vectorization to convert the survey comments and predefined topics into vectors. It then calculates the cosine similarity between each comment and the topics, selecting the top 3 most similar topics for each comment. You can adjust the n_classes parameter to classify a different number of topics per comment.

The code classifies a survey comment to "Other Topic" based on the cosine similarity between the comment and the predefined topics. If a comment doesn't closely match any of the specific topics, it might have a higher similarity score with "Other Topic."

Here's a more detailed explanation:

Vectorization: The TfidfVectorizer converts both the predefined topics and the survey comments into numerical vectors based on the importance of words.
Cosine Similarity: For each survey comment, the code calculates the cosine similarity between the comment vector and each topic vector. This measures how similar the comment is to each topic.
Top Matches: The code then identifies the top 3 topics with the highest similarity scores for each comment.

If a comment doesn't strongly match any specific topic, the similarity score for "Other Topic" might be among the highest, leading to its classification under "Other Topic."

For example, if a comment is about something not covered by the predefined topics, it might not have high similarity scores with any of them, but it could still have a relatively higher score with "Other Topic" due to the general nature of this category.

Would you like to see an example with a specific comment?

Got it! To validate if the existing topics will cover a new survey, you can follow these steps:

Analyze Topic Coverage: Check how well the predefined topics match the new survey comments. This involves calculating the similarity between the new comments and the predefined topics.
Identify Uncovered Areas: Determine if there are significant portions of the new survey comments that don't fit well into any of the predefined topics. This can be done by looking at the similarity scores and identifying comments with low scores across all topics.
Adjust Topics if Necessary: If you find that many comments don't fit well into the existing topics, you may need to adjust your predefined topics or add new ones.

Here's a step-by-step approach with code:

Step 1: Vectorize New Survey Comments¶

# New survey comments for testing
new_survey_comments = [
    "The office environment is very comfortable and the management is approachable.",
    "I am not satisfied with the salary and the benefits provided.",
    "There is a lack of communication and the work-life balance is poor.",
    "I appreciate the career growth opportunities and the training programs.",
    "The company culture is inclusive and supportive."
]

# Preprocess and vectorize the new survey comments
new_comment_vectors = vectorizer.transform(new_survey_comments)

Step 2: Calculate Similarity Scores¶

# Calculate similarity scores for new survey comments
def calculate_similarity_scores(comments, topic_vectors):
    similarity_scores = []

    for comment_vector in comments:
        # Calculate cosine similarity between comment and topics
        similarities = cosine_similarity(comment_vector, topic_vectors).flatten()
        similarity_scores.append(similarities)

    return similarity_scores

# Get similarity scores for new survey comments
similarity_scores = calculate_similarity_scores(new_comment_vectors, topic_vectors)

Step 3: Analyze Coverage¶

# Analyze coverage of predefined topics
def analyze_coverage(similarity_scores, threshold=0.2):
    uncovered_comments = []

    for i, scores in enumerate(similarity_scores):
        # Check if all similarity scores are below the threshold
        if all(score < threshold for score in scores):
            uncovered_comments.append(i)

    return uncovered_comments

# Identify comments that are not well covered by predefined topics
uncovered_comments = analyze_coverage(similarity_scores)

# Print results
for i in uncovered_comments:
    print(f"Uncovered Comment: {new_survey_comments[i]}")

Step 4: Adjust Topics if Necessary¶

If you find that several comments are not well covered by the existing topics, you might need to:

Add New Topics: Introduce new topics that better capture the themes in the new survey comments.
Refine Existing Topics: Adjust the definitions or scope of existing topics to better match the new data.

By following these steps, you can validate whether your existing topics are sufficient for the new survey and make necessary adjustments to improve coverage.

Step 1: Generate Seed Keywords and Descriptions¶

Work Environment
Seed Keywords: office, workspace, environment, conditions, safety
Description: Refers to the physical and psychological conditions of the workplace, including safety, comfort, and overall atmosphere.
Compensation and Benefits
Seed Keywords: salary, benefits, pay, compensation, perks
Description: Concerns the financial and non-financial rewards provided to employees, such as salary, bonuses, health insurance, and other perks.
Management and Leadership
Seed Keywords: management, leadership, supervisor, boss, direction
Description: Involves the effectiveness, behavior, and style of managers and leaders within the organization.
Career Growth and Development
Seed Keywords: career, growth, development, promotion, advancement
Description: Focuses on opportunities for professional development, career advancement, and skill enhancement.
Work-Life Balance
Seed Keywords: work-life, balance, flexibility, hours, personal time
Description: Pertains to the balance between work responsibilities and personal life, including flexible working hours and remote work options.
Company Culture
Seed Keywords: culture, values, mission, environment, inclusivity
Description: Relates to the company's values, mission, and overall cultural environment, including inclusivity and diversity.
Job Satisfaction
Seed Keywords: satisfaction, happiness, job, role, contentment
Description: Measures the level of contentment and fulfillment employees feel in their job roles.
Communication
Seed Keywords: communication, feedback, information, clarity, updates
Description: Involves the effectiveness and clarity of communication within the organization, including feedback mechanisms.
Training and Development
Seed Keywords: training, development, learning, skills, education
Description: Concerns the availability and quality of training programs and opportunities for skill development.
Job Security
- Seed Keywords: security, stability, job, position, layoffs
- Description: Relates to the stability and security of employees' job positions and the organization's future outlook.

Step 2: Create and Evaluate the Model¶

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
import numpy as np

# Define topics with their seed keywords and descriptions
topics = {
    "Work Environment": ["office", "workspace", "environment", "conditions", "safety"],
    "Compensation and Benefits": ["salary", "benefits", "pay", "compensation", "perks"],
    "Management and Leadership": ["management", "leadership", "supervisor", "boss", "direction"],
    "Career Growth and Development": ["career", "growth", "development", "promotion", "advancement"],
    "Work-Life Balance": ["work-life", "balance", "flexibility", "hours", "personal time"],
    "Company Culture": ["culture", "values", "mission", "environment", "inclusivity"],
    "Job Satisfaction": ["satisfaction", "happiness", "job", "role", "contentment"],
    "Communication": ["communication", "feedback", "information", "clarity", "updates"],
    "Training and Development": ["training", "development", "learning", "skills", "education"],
    "Job Security": ["security", "stability", "job", "position", "layoffs"],
    "Other Topic": []
}

# Sample survey comments and their topics
data = {
    'comment': [
        "I love the flexible work hours.",
        "The management is supportive.",
        "Great work-life balance.",
        "Need better communication from the top.",
        "More opportunities for growth would be nice."
    ],
    'topics': [
        ["Work-Life Balance"],
        ["Management and Leadership"],
        ["Work-Life Balance"],
        ["Communication"],
        ["Career Growth and Development"]
    ]
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Binarize the labels
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer(classes=list(topics.keys()))
y = mlb.fit_transform(df['topics'])

# Vectorize the comments and seed keywords
vectorizer = TfidfVectorizer()
all_text = list(df['comment']) + [kw for kws in topics.values() for kw in kws]
vectorizer.fit(all_text)
X = vectorizer.transform(df['comment'])

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = OneVsRestClassifier(LogisticRegression(max_iter=1000))
model.fit(X_train, y_train)

# Predict on the test set
y_pred = model.predict(X_test)

# Evaluate the model
print(classification_report(y_test, y_pred, target_names=mlb.classes_))

# Function to predict topics with an "Other" category
def predict_with_other(model, vectorizer, mlb, comments, threshold=0.2):
    X_new = vectorizer.transform(comments)
    probabilities = model.predict_proba(X_new)
    predictions = (probabilities >= threshold).astype(int)

    # If no topic exceeds the threshold, classify as "Other"
    for i in range(predictions.shape[0]):
        if not np.any(predictions[i]):
            predictions[i] = np.zeros(predictions.shape[1])
            predictions[i, -1] = 1  # Assuming the last column is "Other Topic"

    return mlb.inverse_transform(predictions)

# Example prediction
new_comments = ["I appreciate the career development programs.", "The cafeteria food needs improvement."]
predicted_topics = predict_with_other(model, vectorizer, mlb, new_comments)
print(predicted_topics)

Step 3: Test the Model on New Survey Data¶

# New survey comments for testing
new_survey_comments = [
    "The office environment is very comfortable and the management is approachable.",
    "I am not satisfied with the salary and the benefits provided.",
    "There is a lack of communication and the work-life balance is poor.",
    "I appreciate the career growth opportunities and the training programs.",
    "The company culture is inclusive and supportive."
]

# Preprocess and vectorize the new survey comments
new_comment_vectors = vectorizer.transform(new_survey_comments)

# Calculate similarity scores for new survey comments
def calculate_similarity_scores(comments, topic_vectors):
    similarity_scores = []

    for comment_vector in comments:
        similarities = cosine_similarity(comment_vector, topic_vectors).flatten()
        similarity_scores.append(similarities)

    return similarity_scores

# Get similarity scores for new survey comments
similarity_scores = calculate_similarity_scores(new_comment_vectors, topic_vectors)

# Analyze coverage of predefined topics
def analyze_coverage(similarity_scores, threshold=0.2):
    uncovered_comments = []

    for i, scores in enumerate(similarity_scores):
        if all(score < threshold for score in scores):
            uncovered_comments.append(i)

    return uncovered_comments

# Identify comments that are not well covered by predefined topics
uncovered_comments = analyze_coverage(similarity_scores)

# Print results
for i in uncovered_comments:
    print(f"Uncovered Comment: {new_survey_comments[i]}")

Step 4: Explain Criteria for Using the Model on New Data¶

To determine if the model can be used on new data: - Topic Coverage: Ensure most new comments are well-covered by predefined topics. - Performance Metrics: Check precision, recall, and F1-score on new data. - Manual Review: Manually verify the relevance of predicted topics. - Feedback Loop: Continuously improve the model based on feedback from new data.

Each row includes seed keywords and a brief description that outlines the essence of each topic.

Enhanced Table for Topic Modeling:¶

Here is the continuation of the enhanced table after "Regulatory Requirements":

Primary Topic	Sub-Topic	Keywords	Description
Career	Career Advancement	promotion, raise, growth, leadership role, skills development	Opportunities and pathways for employees to advance in their roles and take on higher responsibilities within the organization.
Career	Career Mobility	internal transfer, relocation, role change, job rotation	The ability of employees to move between roles, departments, or locations within the organization.
Career	Career Opportunities	potential, open roles, promotion chance, development plan	Access to roles and opportunities for employees to progress professionally and achieve their career goals.
Career	Training and Professional Development	learning, upskilling, courses, certifications, mentorship	Programs and initiatives to enhance employees' skills, knowledge, and competencies through structured learning.
Company Direction & Strategy	Business Decisions	strategic planning, management decisions, leadership choices	Decisions made at the organizational level that influence the company’s direction and goals.
Company Direction & Strategy	Company Direction	vision, mission, long-term goals, organizational strategy	The overall strategy and roadmap for achieving the company's mission and vision.
Company Direction & Strategy	Vision	future outlook, long-term goals, inspiration, mission statement	The organization’s aspirational goals and its commitment to achieving them.
Company Direction & Strategy	Senior Leadership	executives, C-suite, decision-makers, leadership support	The effectiveness and approachability of senior leaders in guiding the company and supporting employees.
Diversity, Equity, & Inclusion	Diversity, Equity, & Inclusion	equal opportunity, representation, fairness, belonging, inclusive culture	Efforts to foster an inclusive environment where employees feel valued regardless of their background.
Employee Benefits	Employee Benefits	perks, benefits, wellness programs, incentives	The non-salary compensation provided to employees, such as insurance, wellness programs, and additional perks.
Employee Benefits	Paid Time Off	vacation, holidays, PTO, sick leave	Time off provided to employees as part of their compensation package.
Employee Benefits	Pay and Compensation	salary, bonus, remuneration, incentive structure	The monetary and non-monetary rewards employees receive for their work.
Employee Benefits	Sick Leave	health leave, illness absence, PTO for illness	Policies and provisions for employees to take leave for health reasons.
Employee Experience and Engagement	Communication and Transparency	open communication, honesty, clarity, feedback channels	The level of openness and clarity in communication between employees and leadership.
Employee Experience and Engagement	Employee Voice	feedback, suggestions, employee opinion, participation	Platforms and opportunities for employees to express their views and provide input.
Employee Experience and Engagement	Low Morale	disengagement, dissatisfaction, unhappiness, lack of motivation	Signs of low employee satisfaction, which can lead to decreased productivity and retention.
Employee Experience and Engagement	Negative Work Experience	workplace issues, conflicts, unfair treatment	Instances where employees have had adverse experiences in the workplace.
Employee Experience and Engagement	Org Culture	values, team dynamics, workplace culture	The prevailing attitudes, values, and practices that define the work environment.
Employee Experience and Engagement	Positive Work Experience	teamwork, recognition, collaboration, achievement	Instances where employees have had fulfilling and supportive experiences at work.
Employee Experience and Engagement	Intent to Leave	resignation, turnover, quitting, job change	Indicators that employees are considering leaving the organization.
Employee Experience and Engagement	Sense of Value	recognition, appreciation, importance	The extent to which employees feel valued and recognized for their contributions.
Employee Experience and Engagement	Team Culture	collaboration, teamwork, support, group dynamics	The environment and behaviors within teams that impact collaboration and effectiveness.
Job Security	Job Stability	job safety, long-term employment, economic stability	The perceived and actual assurance of continued employment.
Life Event	Internship	apprenticeship, training program, temporary position	Early-career opportunities for learning and gaining professional experience.
Life Event	Retirement	pension, end of career, retirement benefits	Transitioning out of the workforce and the associated support and benefits.
Manager or Supervisor	Manager Effectiveness	leadership skills, decision-making, team support, coaching	The ability of managers to effectively lead, support, and guide their teams.
No Topic	Insufficient Information	unclear, incomplete, vague responses	Feedback or survey responses that lack enough context or clarity for analysis.
Other Topic	No Topic Match	unrelated, irrelevant, off-topic	Feedback or responses that do not align with predefined topics.
Performance Management	Customer Service	client satisfaction, service delivery, client support	Employees’ feedback on their roles in delivering excellent customer service.
Performance Management	Job Expectations	performance goals, role clarity, task expectations	Clarity and alignment between employees’ roles and organizational goals.
Performance Management	Recognition	awards, rewards, employee of the month	Systems and practices for acknowledging employee contributions and achievements.
Performance Management	Performance Reviews	appraisal, feedback, evaluations	The process and effectiveness of reviewing and providing feedback on employee performance.
Policies & Procedures	Non-Compliance	policy violation, misconduct, rule-breaking	Instances where employees or processes do not align with company policies.
Policies & Procedures	Red Tape	bureaucracy, slow processes, inefficiency	Excessive procedures or regulations that impede efficiency and progress.
Policies & Procedures	Regulatory Requirements	compliance, laws, industry standards	Adherence to external and internal legal or regulatory requirements.
Policies & Procedures	Process Changes	new policies, updated procedures, workflow changes	Modifications to existing processes and their impact on employees and operations.
Policies & Procedures	Risk Management	risk mitigation, safety protocols, hazard prevention	Strategies and protocols implemented to manage and mitigate risks within the organization.
Policies & Procedures	Wells Fargo Policies	company policies, guidelines, compliance standards	Specific guidelines and policies unique to Wells Fargo or any other employer in context.
Talent Acquisition	Internal Hiring	internal mobility, internal promotion, internal transfers	Opportunities for existing employees to fill open positions within the company.
Talent Acquisition	Onboarding	new hire, orientation, training for new employees	The process of integrating new hires into the organization, including training and orientation.
Talent Acquisition	Recruitment Process	hiring, interviews, candidate selection	The practices and processes involved in attracting and selecting candidates for open roles.
Technology	Need for Improvement	system upgrade, software issues, technology enhancement	Areas where technological tools or systems require enhancement to better support employee workflows.
Technology	Technology Process Improvement	automation, digital transformation, streamlined tools	Initiatives to improve the efficiency and functionality of technology processes within the organization.
Technology	Technology	IT infrastructure, technical tools, systems	General employee feedback on technology use, accessibility, and effectiveness in the workplace.
Unfair Work Environment	Discrimination	bias, inequality, racism, unfair treatment	Instances of unfair or unequal treatment based on personal characteristics, such as race, gender, or age.
Unfair Work Environment	Favoritism	unequal opportunity, preferential treatment, nepotism	Concerns related to favoritism or biased treatment within teams or departments.
Unfair Work Environment	Retaliation	workplace retaliation, unfair repercussions	Situations where employees feel punished for raising concerns or complaints.
Unfair Work Environment	Toxic Work Environment	hostility, workplace conflict, toxic behavior	Feedback on negative or harmful work environments characterized by lack of support, bullying, or other issues.
Unfair Work Environment	Unethical Behavior	policy violations, workplace misconduct, ethical breaches	Concerns about actions or decisions that violate ethical or professional standards.
Wellbeing	Medical Related	medical leave, health issues, doctor appointments	Health-related issues that affect employees’ ability to work, including access to medical support.
Wellbeing	Mental Health	stress, anxiety, depression, burnout	Feedback on how workplace practices and policies impact employees’ mental health and wellbeing.
Wellbeing	Psychological Safety	safe space, open dialogue, fear of retaliation	The extent to which employees feel safe to speak openly and take risks without fear of negative consequences.
Wellbeing	Workplace Safety	safety measures, hazard prevention, accidents	Concerns about physical safety in the workplace, including protocols for preventing accidents and ensuring employee well-being.
Work Balance	Overworked	long hours, excessive workload, unmanageable tasks	Instances where employees feel overwhelmed due to excessive work demands.
Work Balance	Understaffed	lack of resources, manpower shortage, team overextension	Issues stemming from insufficient staffing to meet workload demands.
Work Balance	Burn-out	exhaustion, mental fatigue, stress	Chronic stress and physical or emotional exhaustion from work demands.
Work Balance	Work Life Balance	personal time, family time, flexible hours	Feedback on the ability to balance work responsibilities with personal and family needs.
Work Location Strategy	Location Strategy	relocation, hybrid model, office location	Policies or strategies related to work locations, including remote work, hybrid work, or office relocations.
Work Location Strategy	Relocation	moving, job transfer, change in location	Employee feedback on the impact of relocation policies on their personal and professional lives.
Work Location Strategy	Working From Home	remote work, virtual workplace, home office setup	Feedback on the challenges and benefits of working remotely or in hybrid setups.

Last update 2024-12-09