Task-shifting within health care systems – a general review of the literature and implications for mental healthcare
Bibliographic record
Abstract
Background There have been a growing interest in the effectiveness of task-shifting as a strategy for targeting expanding health care demands in settings with shortages of qualified health personnel. Aims To explore the reasons for task-shifting and the healthcare settings in which task-shifting are successfully applied as well as the challenges associated with task shifting. Methods Literature searches were conducted on PubMed and Google Scholar using the search term – ‘Task shifting’ and Task-shifting’. Results Reasons for task-shifting including: a reduction in the time needed to scale up the health workforce, improving the skill mix of teams, lowering the costs for training and remuneration, supporting the retention of existing cadres by reducing burnout from inefficient care processes and mitigating a health system's dependence on highly skilled individuals for specific services. Clinical settings in which task-shifting models of care have been successfully implemented, include: HIV/AIDS care, epilepsy and tuberculosis care, hypertension and diabetes care and mental healthcare. Finally, challenges which hinder the successful implementation of task-shifting models of care, include professional and institutional resistance, concern about the quality of care provided by lower lever health cadres and lack of regulatory and policy frameworks as well as funding to support task-shifting programmes. Conclusion The review brings to light important health policy and research priorities which can be explored to identify the feasibility of using task-shifting models of care to address the critical shortage of health personnel in managing emerging communicable and non-communicable diseases, including opportunities for expanding mental health care in conflict and under-resourced regions globally. Disclosure of interest The author has not supplied his/her declaration of competing interest.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".