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Record W2110638335 · doi:10.1186/1478-4491-12-67

Needs-based human resources for health planning in Jamaica: using simulation modelling to inform policy options for pharmacists in the public sector

2014· article· en· W2110638335 on OpenAlex
Gail Tomblin Murphy, Adrian MacKenzie, Joan Guy-Walker, Claudette Walker

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHuman Resources for Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsDalhousie University
FundersHealth CanadaPan American Health Organization
KeywordsWorkforceGeneral partnershipHuman resourcesHealth services researchHealth careContext (archaeology)Public healthHealth policyPharmacyHealth administrationBusinessPublic sectorEssential medicinesMedicineEconomic growthNursingPolitical scienceEconomicsFinanceGeographyManagement

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.251
GPT teacher head0.535
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it