Needs-based human resources for health planning in Jamaica: using simulation modelling to inform policy options for pharmacists in the public sector
Bibliographic record
Abstract
BACKGROUND: Planning for human resources for health (HRH) is central to health systems strengthening around the world, including in the Caribbean and Jamaica. In an effort to align Jamaica's health workforce with the changing health needs of its people, a partnership was established between Jamaican and Canadian partners. The purpose of the work described in this paper is to describe the development and application of a needs-based HRH simulation model for pharmacists in Jamaica's largest health region. METHODS: Guided by a Steering Committee of Jamaican stakeholders, a simulation modelling approach originally developed in Canada was adapted for the Jamaican context. The purpose of this approach is to promote understanding of how various factors affect the supply of and/or requirements for HRH in different scenarios, and to identify policy levers for influencing each of these under different future scenarios. This is done by integrating knowledge of different components of the health care system into a single tool that shows how changes to different parameters affect HRH supply or requirements. Data to populate the model were obtained from multiple administrative databases and key informants. Findings were validated with the Steering Committee. RESULTS: The model estimated an initial shortage of 110 full-time equivalent (FTE) pharmacists in the South East Region that, without intervention, would increase to a shortage of about 150 FTEs over a 15-year period. In contrast to the relatively small impact of a large enrollment increase in Jamaica's pharmacy training programme, interventions to increase recruitment of pharmacists to the public sector, or improve productivity - through, for example, the use of support staff and/or new technologies - may have much greater impact on reducing this shortage. CONCLUSIONS: The model represents an improvement on the HRH planning tools previously used in Jamaica in that it supports the estimation of HRH requirements based directly on measures of population health need. Both the profession (pharmacists) and country (Jamaica) considered here are under-studied. Further investments by Jamaica's MoH in continuing to build capacity to use such models, in combination with their efforts to enhance health information systems, will support better informed HRH planning in Jamaica.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".