Public health program evaluation best practices within the Canadian federal government [research project] / by Agate Stankiewicz.
Bibliographic record
Abstract
Program evaluation findings are reported in evaluation reports as part of Treasury Board \nof Canada Secretariat (TBS) funding requirements and are key information used to ensure \naccountability for planned results. This project critically appraises the Public Health \nAgency of Canada?s (PHAC) eight program evaluation reports for their strengths and \nweaknesses - program evaluation planning, design and implementation, data collection \nand analysis, and reporting - for informing public health practice. First, these reports are \nappraised using a modified version of the review template obtained from the ?Review of \nthe Quality of Evaluations Across Departments and Agencies?, developed by the TBS. \nThese findings are then reviewed in light of public health program evaluation guidelines \nfor compliance with the standards of public health evidence, as well as the current TBS \nEvaluation Policy (2001) for compliance with the standards of performance reporting. \nThe project concludes with recommendations to advance public health program \nevaluation planning, design and implementation, data collection and analysis, and \nreporting in the joint context of public health practice and the Canadian federal \ngovernment accountability for performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.192 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.021 |
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".