11. Who is in charge? Introducing criteria to evaluate health human resource (HHR) policy documents
Bibliographic record
Abstract
The maldistribution of and lack of access to health professionals continues to be a major issue for policymakers at all levels of government. Additionally, the basis by which Health Human Resource (HHR) policy is determined is unclear. Publications found in independent reports, peer-reviewed journals and most importantly, grey literature, can significantly influence or inform major policy decisions for “hot button” HHR issues (1) . We propose a framework that can be used to classify, rank and evaluate HHR policy/planning documents. Our framework creates six major criteria that are used to evaluate policy documents. These criteria are: 1) literature review, 2) source of primary information, 3) nature of recommendations, 4) implementation strategies, 5) credibility of authors and 6) credibility of publisher. Within each category, a score from zero to three (for criteria 1-4) or zero to two (criteria 5-6) is assigned, depending on the caliber of the document. Summing the scores from each section yields a document’s overall score. The intent of this measure is two-fold. Firstly, we want to create a tool that can be widely utilized by policymakers to help inform their decisions. Secondly, it can be used as a springboard to stimulate discussion and debate around HHR planning and policy formulation. National Information Center on Health Services Research and Health Care Technology. (NICHSR) Health Services Research and Health Policy Grey Literature Project: Summary Report. 2006. http://www.nlm.nih.gov/ nichsr/greylitreport_06.html. Accessed February 20, 2007.
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 | 0.186 | 0.377 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.038 | 0.028 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.038 | 0.024 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".