Narrowing the gap between eye care needs and service provision: a model to dynamically regulate the flow of personnel through a multiple entry and exit training programme
Bibliographic record
Abstract
BACKGROUND: The purpose of this paper is to present a complex yet transparent, computable model to simulate the regulation of the flow of personnel through a previously described multiple-entry, multiple-exit eye care training scheme linked to the health workforce. This methodology should be a useful tool for the planner; it can address changes and feedbacks over time and be sensitive to any unexpected consequences of the interactions. The same model template can be applied to calculate the finances associated with the personnel flow. PRESENTATION OF THE HYPOTHESIS: The worth of any model or set of concepts of human resources for health is considerably enhanced by actual field application. However, implementation involves the selection of one set of parameters and a large, long-term commitment of resources. A far less expensive and time-consuming, yet still effective, method of testing assumptions and ideas would be to simulate their application using a variety of possible inputs, structural configurations and/or desired outcomes. To that end, this paper presents a computable, dynamic model of personnel flows within a health system. TESTING THE HYPOTHESIS: Some testing of the model has been demonstrated in a previous paper. However, the value of the model is that all stakeholders can enter their own data and parameter assumptions and readily review the outcomes. IMPLICATIONS OF THE HYPOTHESIS: The complex yet easy-to-use model presented in this paper opens the debate on current and future policy to any stakeholder. A very wide range of scenarios can be considered and a selected option can be monitored and changed dynamically over time.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".