Bibliographic record
Abstract
OBJECTIVE: To develop a framework for professional practice for a large urban public health unit in Canada. METHODS: The project involved a literature search, key informant interviews, an environmental scan and focus groups. RESULTS: Analysis and synthesis led to recommendations for the development of discipline-specific Professional Practice Leaders (PPL) and an Interprofessional Practice Leaders Network. The latter meets to discuss cross-cutting practice issues and is chaired by the chief executive officer of the health unit, the Medical Officer of Health. The one-year evaluation has demonstrated that this initiative has worked well in practice. It is a flexible framework which provides new leadership opportunities and gives staff valuable input into decision-making on practice issues. It is also a more efficient use of staff resources, including a comprehensive approach to solving problems and in breaking down silos between programs. Communication and collaboration between disciplines has increased. CONCLUSION: The initiative was evaluated successfully after the pilot year. In going forward areas to review include the time allotment for the PPL, communication between the PPL, the respective Program Director and the entire department, and expanding professional development opportunities for the PPL.
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 | 0.078 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.049 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".