Healthcare Provider Education: From Institutional Boxes to Dynamic Networks
Bibliographic record
Abstract
The world recognizes the need for close collaboration in planning between the healthcare system and the post-secondary education system; this has also been advocated in the lead article. Forums and mechanisms to facilitate this collaboration are being implemented from local to global environments. Beyond the focus on competency gaps, there are important functional co-dependencies between healthcare and post-secondary education, including the need for a more formalized continuous quality improvement approach at the inter-organizational system level. The case for this close and continuous collaborative relationship is based on the following: (1) a close functional relationship, (2) joint responsibility for healthcare provider education, (3) the urgent need to address the workforce and education strategies for almost all healthcare services areas and (4) the factors that characterize successful and sustained quality improvement in complex adaptive systems. A go-forward vision consisting of an integrated web of academic health networks is proposed, each with its particular shared vision and aligned with an overall vision for healthcare in each provincial jurisdiction, as well as with national and global healthcare objectives.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.040 | 0.038 |
| Insufficient payload (model declined to judge) | 0.012 | 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".