Workforce Planning Models for Oral Health Care: A Scoping Review
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: For health care services to address the health care needs of populations and respond to changes in needs over time, workforces must be planned. This requires quantitative models to estimate future workforce requirements that take account of population size, oral health needs, evidence-based approaches to addressing needs, and methods of service provision that maximize productivity. The aim of this scoping review was to assess whether and how these 4 elements contribute to existing models of oral health workforce planning. METHODS: A scoping review was conducted. MEDLINE, Embase, HMIC, and EconLit were searched, all via OVID. Additionally, gray literature databases were searched and key bodies and policy makers contacted. Workforce planning models were included if they projected workforce numbers and were specific to oral health. No limits were placed on country. A single reviewer completed initial screening of abstracts; 2 independent reviewers completed secondary screening and data extraction. A narrative synthesis was conducted. RESULTS: A total of 4,009 records were screened, resulting in 42 included articles detailing 47 models. The workforce planning models varied significantly in their use of data on oral health needs, evidence-based services, and provider productivity, with most models relying on observed levels of service utilization and demand. CONCLUSIONS: This review has identified quantitative workforce planning models that aim to estimate future workforce requirements. Approaches to planning the oral health workforce are not always based on deriving workforce requirements from population oral health needs. In many cases, requirements are not linked to population needs, while in models where needs are included, they are constrained by the existence and availability of the required data. It is critical that information systems be developed to effectively capture data necessary to plan future oral health care workforces in ways that relate directly to the needs of the populations being served. KNOWLEDGE TRANSFER STATEMENT: Policy makers can use the results of this study when making decisions about the planning of oral health care workforces and about the data to routinely collect within health services. Collection of suitable data will allow for the continual improvement of workforce planning, leading to a responsive health service and likely future cost savings.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it