“Food engages people, as we know”: health care and service providers’ experiences of using food as an incentive in HIV care and support in British Columbia, Canada
Bibliographic record
Abstract
Food insecurity is widely documented among people living with HIV (PLWH) worldwide, and it presents significant challenges across the spectrum of HIV care and support. In North America, the prevalence of food insecurity among PLWH exceeds 50%. In the province of British Columbia (BC), it exceeds 65%. It comes as no surprise that food has become an essential tool in supporting and engaging with PLWH. Over the past decade, however, a shift has taken place, and food has become an incentive to boost uptake and outcomes of prevention, testing, treatment, and support. To explore this practice, we drew on a qualitative case study of incentives in the care and support of PLWH. This paper presents the findings of a targeted analysis of interviews (N = 25) that discuss food incentives and explores two main themes that shed light on this practice: (1) Using food to engage versus to incentivize and (2) Food is more beneficial and more ethical. Providers perceived food more positively than other incentives, despite the goal remaining somewhat the same. Incentives, such as cash or gift cards, were considered ethically problematic and less helpful (and potentially harmful), whereas food addressed a basic need and felt more ethical.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".