The heterogeneity of vulnerability in public health: a heat wave action plan as a case study
Bibliographic record
Abstract
The concept of vulnerability is frequently used in public health policies to develop tailored interventions or dedicate proportionately more resources to certain sub-populations. However, once segments of the population are identified as vulnerable, they are rarely consulted regarding whether this label is acceptable before instituting interventions. Instead, it is implicitly assumed that the targeted individuals identify themselves as vulnerable and experience an unambiguous and consistent need for public health assistance. In this paper, using public health interventions during heat waves as a case study, we question such assumptions. A qualitative study was conducted in Montreal, Canada involving two focus groups among populations specifically targeted by the heat action plan as vulnerable: one composed of individuals diagnosed with schizophrenia, and one composed of individuals who have alcohol or drug addictions. Findings revealed significant heterogeneity in the definition and experience of vulnerability as it is used in the context of a heat action plan in Montreal. We found differences between the two focus groups in several areas including sources of information they had access to within the heat action plan measures and their perspectives regarding the appropriateness of specific measures in the heat action plan. We then observed differences within each of the focus groups in several areas including their social networks relationships. The concept of vulnerability is often used in public health policies. Yet, while this concept may be convenient for shaping policies to reduce inequalities in health, the heterogeneity of populations defined as vulnerable should not be underestimated.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".