The needs-based health workforce planning method: a systematic scoping review of analytical applications
Bibliographic record
Abstract
Although the theoretical underpinnings and analytical framework for needs-based health workforce planning are well developed and tested, its uptake in national planning processes is still limited. Towards the development of open-access needs-based planning model for national workforce planning, we conducted a systematic scoping review of analytical applications of needs-based health workforce models. Guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses-extension for Scoping Reviews (PRISMA-ScR) checklist, a systematic scoping review was conducted. A systematic search of peer-reviewed literature published in English was undertaken across several databases. Papers retrieved were assessed against predefined inclusion criteria, critically appraised, extracted and synthesized. Twenty-five papers were included, which showed increasing uptake of the needs-based health workforce modelling, with 84% of the studies published within the last decade (2010-20). Three countries (Canada, Australia and England) accounted for 48% of the publications included whilst four studies (16%) were based on low-and-middle-income countries. Only three of the studies were conducted in sub-Saharan Africa. Most of the studies (36%) reported analytical applications for specific disease areas/programs at sub-national levels; 20% focused on the health system need for particular categories of health workers, and only two (8%) reported the analytical application of the needs-based health workforce approach at the level of a national health system across several disease areas/programs. Amongst the studies that conducted long-term projections, the time horizon of the projection was an average of 17 years, ranging from 3 to 33 years. Most of these studies had a minimum time horizon of 10 years. Across the studies, we synthesized six typical methodological considerations for advancing needs-based health workforce modelling. As countries aspire to align health workforce investments with population health needs, the need for some level of methodological harmonization, open-access needs-based models and guidelines for policy-oriented country-level use is not only imperative but urgent.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".