Care Redistribution: Reassembling Task Shifting with Naloxone Distribution and First Aid
Bibliographic record
Abstract
This dissertation develops the conceptual connection between first aid and task shifting and task sharing (TS/S), and advances a conceptual framework for the redistribution of health care services, especially where those practices involve lay providers. The dissertation uses overdose education and naloxone distribution as a leading example of an intervention at the intersection of first aid and TS/S to problematize the World Health Organization’s dominant definition and conceptualization of TS/S. The first study is a systematic review on the health effects of first aid and TS/S with laypeople in underserved populations and low-resource settings. The review concludes that lay responders may improve patient morbidity and mortality, and build community capacity to manage health emergencies including trauma, burns, cardiac arrest, opioid poisoning, malaria, paediatric communicable diseases and malnutrition. The second study is a mixed methods study to establish the feasibility of recruitment and retention strategies for a randomized trial on overdose education and naloxone distribution involving participants who are likely to witness opioid overdose. The study met its feasibility goals by recruiting 30 participants over 24 days and retaining 21 participants (retention rate 70%, 95% CI 56.7% – 100%). Study procedures were acceptable to participants, and the randomized trial deemed feasible for implementation. In the third paper, a Delphi process was used to achieve consensus on the purpose of TS/S and characteristics of contexts amenable to TS/S, and then develop a revised conceptual framework for TS/S programmes. The framework offers a refined definition of TS/S and a general purpose statement to guide TS/S programmes. The framework also proposes five 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. The dissertation contends that a broader, more inclusive conceptual assembly is required to capture the diversity of redistributive programmes and the history, politics, actors, and goals involved in the redistribution of care. The dissertation proposes “care redistribution” as a more suitable assembly. Care redistribution occurs when communities deliberately reallocate or reassign heath care practices, functions, and relationships to enhance care, improve health, or redress inequities.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".