Difficult-to-fill vacancies in selected health care disciplines in British Columbia, 1980-1991
Bibliographic record
Abstract
This report is the second of its kind presenting a summary and synthesis of the data collected by the Health Human Resources Unit (HHRU) on difficult-to-fill (DTF) vacancies in British Columbia. The first summary report was completed in 1987 following the first seven years of data collection; this report incorporates five more years of continuous data, covering a twelve-year period. The "Difficult-to-Fill" study began in January 1980 following discussions in 1979 between the British Columbia Health Association (BCHA) and the Health Human Resources Working Group (HHRWG), at the time comprising the ministries of Health, Post-Secondary Education. Universities, and Labour. These discussions were undertaken in response to mounting pressure from numerous reports in British Columbia newspapers during 1979 and 1980 of a nursing shortage. However, there was no available empirical evidence at the time for these claims. This project was thus established to monitor unmet demand for health personnel in British Columbia with a focus on nursing personnel but also to include a large number of other health care disciplines.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".