Conceptual framework for task shifting and task sharing: an international Delphi study
Bibliographic record
Abstract
BACKGROUND: Task shifting and sharing (TS/S) involves the redistribution of health tasks within workforces and communities. Conceptual frameworks lay out the key factors, constructs, and variables involved in a given phenomenon, as well as the relationships between those factors. Though TS/S is a leading strategy to address health worker shortages and improve access to services worldwide, a conceptual framework for this approach is lacking. METHODS: We used an online Delphi process to engage an international panel of scholars with experience in knowledge synthesis concerning TS/S and develop a conceptual framework for TS/S. We invited 55 prospective panelists to participate in a series of questionnaires exploring the purpose of TS/S and the characteristics of contexts amenable to TS/S programmes. Panelist responses were analysed and integrated through an iterative process to achieve consensus on the elements included in the conceptual framework. RESULTS: The panel achieved consensus concerning the included concepts after three Delphi rounds among 15 panelists. The COATS Framework (Concepts and Opportunities to Advance Task Shifting and Task Sharing) offers a refined definition of TS/S and a general purpose statement to guide TS/S programmes. COATS describes that opportunities for health system improvement arising from TS/S programmes depending on the implementation context, and enumerates eight necessary conditions and important considerations for implementing TS/S programmes. CONCLUSION: The COATS Framework offers a conceptual model for TS/S programmes. The COATS Framework is comprehensive and adaptable, and can guide refinements in policy, programme development, evaluation, and research to improve TS/S globally.
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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 | 0.190 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".