Conditions underpinning success in joint service-education workforce planning
Bibliographic record
Abstract
Vancouver Island lies just off the southwest coast of Canada. Separated from the large urban area of Greater Vancouver (estimated population 2.17 million) by the Georgia Strait, this geographical location poses unique challenges in delivering health care to a mixed urban, rural and remote population of approximately 730,000 people living on the main island and the surrounding Gulf Islands. These challenges are offset by opportunities for the Vancouver Island Health Authority (VIHA) to collaborate with four publicly funded post-secondary institutions in planning and implementing responses to existing and emerging health care workforce needs. In this commentary, we outline strategies we have found successful in aligning health education and training with local health needs in ways that demonstrate socially accountable outcomes. Challenges encountered through this process (i.e. regulatory reform, post-secondary policy reform, impacts of an ageing population, impact of private, for-profit educational institutions) have placed demands on us to establish and build on open and collaborative working relationships. Some of our successes can be attributed to evidence-informed decision-making. Other successes result from less tangible but no less important factors. We argue that both rational and "accidental" factors are significant--and that strategic use of "accidental" features may prove most significant in our efforts to ensure the delivery of high-quality health care to our communities.
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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.023 | 0.017 |
| 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".