Toward evidence‐based policy decisions: a case study of nursing health human resources in Ontario, Canada
Bibliographic record
Abstract
Toward evidence‐based policy decisions: a case study of nursing health human resources in Ontario, Canada This paper reflects how health services research ‘evidence’ was used to influence decisions in the province of Ontario, Canada. The process involved interaction among a variety of stakeholders and decision‐makers with researchers to reduce uncertainty and to substantiate emerging service provision issues in the province. The issues presented here focus specifically on an analysis of the nursing situation completed in 1998 for the Minister of Health’s Nursing Task Force, which examined key issues in service delivery. The issues were: restructured work environments; nurse supply and declining enrollments; labour trends and utilization of the nursing workforce; patient acuity and complexity of work environments and the influence on workload; and the paucity of reliable and valid data bases for analysis of nursing’s contribution to the health system. Ontarians can be confident that the Task Force recommendations were born from solid research‐based evidence and now the challenge becomes to monitor the implementation of these resolutions over time.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| 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".