iTRACE: In-Depth Trends and Root Cause Analysis of Canadian Public Service Employee Survey
Bibliographic record
Abstract
Governments continuously implement measures and initiatives to enhance the appeal of careers in public service. Understanding the key factors influencing the satisfaction of public service employees is paramount, as it can lead to enhanced performance, increased employee retention, and the attraction of top talent, ultimately resulting in improved service delivery to the public. Every two years, the Canadian government conducts a survey among public service employees to gauge the effectiveness of current practices and areas needing improvement from the employees' viewpoint. Although strides have been made in utilizing survey findings to inform action plans, their full potential remains untapped. In this study, we propose a multi-layered framework, called iTRACE, to comprehensively analyse the survey data and explore the interrelationships among various factors impacting employee satisfaction. Our findings illuminate concealed patterns in employee responses, pinpoint possible root causes, and offer guidance for targeted improvements. We posit that this approach can assist decision-makers in refining strategies to enhance employee satisfaction and is transferable to other organizations and countries for inferring causal relationships from survey data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".