Using the evaluation process as a lever for improving health and healthcare accessibility: The case of HCV services organization in Quebec
Bibliographic record
Abstract
BACKGROUND: The evaluation process can be a lever to improve pathways of access to healthcare. The objective of this article is to show how an evaluation strategy can both contribute to knowledge development and have direct impacts on health services provision. We use the case of hepatitis C (HCV) services organization to illustrate the use and the value of this evaluative approach. METHOD: Inspired by empowerment evaluation, the transformative-participatory approach involved overlapping phases of knowledge development and discussion with stakeholders. We conducted several knowledge development activities to discern the needs of people with HCV, the resources available, and the facilitators and impediments along the care pathway, starting from prevention and screening, all the way through to treatment. Using an overlapping approach allowed us to regularly transfer acquired knowledge back to the participants in the study settings and also to gather their impressions, interpretations, and suggestions during periods of deliberation. RESULTS: The knowledge development activities made it possible to document the needs, resources, and experiences of people affected by HCV. In the discussion sessions, viable solutions were identified to improve health and healthcare access for people with HCV and to prioritize certain actions. This project demonstrated that using the evaluation process can enable an instrumental, conceptual use of results and, in fact, can have a transformative impact on services organization.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".