Co-developing an inclusive interprofessional health workforce minimum data standard for enhanced planning and decision-making: A Canadian case with international relevance
Bibliographic record
Abstract
BACKGROUND: Comprehensive and standardized health workforce data are the foundation of more robust planning and evidence-informed decision-making in the face of multiple crises. OBJECTIVE: This paper describes the process, results, and lessons learned in co-developing an inclusive, interprofessional health workforce minimum data standard (MDS) for planning. METHODS: A four-phase development process was undertaken: 1) we gathered existing data standards through an environmental scan and literature review, from which we synthesized common data elements into modules; 2) we gathered input through collaborator engagement on the suitability of these data elements to address their priority planning questions; 3) we reviewed the retained data elements with information garnered from an ongoing integrated primary care health workforce planning process; 4) collaborating partners provided detailed feedback on the drafted MDS data elements. RESULTS: Data elements, their sources and other metadata identified from the scans were synthesized into three modules on health worker capacity, education, and identification. Consultation feedback led to refinements and additional data elements. The retrospective review led to a streamlining of the number elements within each module. Partner feedback led to further refinement, mindful of implementation, including dividing them into a core and supplemental set. CONCLUSIONS: Co-developing an MDS for planning benefits from building off existing data standards, open and ongoing collaborator engagement for buy-in, and practical considerations balancing adding more data against finding the right data elements to fit planning needs. Although the MDS was developed for a Canadian context, the approach and outputs are transferable to other settings.
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 | 0.056 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.042 | 0.016 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".