A survey of Web-based health human resource planning activities in Canada.
Bibliographic record
Abstract
Health Human Resource Planning (HHRP) has become a priority for Canadian researchers, policy-makers, and decision-makers. As social, economic, and technological developments propel health care into the information age, Web-based access to HHRP-related information is rapidly assuming greater significance. Convenient access to HHRP-related information is important for current and future HHRP and will continue to be a priority as the area develops and responds to new challenges. This paper identifies Web-based resources of interest to the HHRP community. It provides an overview of key Canadian HHRP activities, with a focus on nursing human resource planning. Policy institutes, research units, governments and government agencies, professional associations and unions, think tanks, universities, and not-for-profit organizations release a number of reports that are seldom integrated into conventional literature vehicles (such as journals or bibliographic databases). The Web sites of these organizations frequently provide access to this unpublished or grey literature. Grey literature is defined by the US Interagency Gray Literature Working Group as open source material that usually is available through specialized channels and may not enter normal channels or systems of publication, distribution, bibliographic control, or acquisition by booksellers or subscription agents (Soule & Ryan, 1995). It includes academic papers, scientific protocols, white papers, preprints, committee reports, proceedings, conference papers, research reports, standards, discussion papers, technical reports, dissertations, theses, government
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.045 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".