A multi-perspective approach for defining neighbourhood units in the context of a study on health inequalities in the Quebec City region
Bibliographic record
Abstract
BACKGROUND: Identification of socioeconomic and health inequalities at the local scale is facilitated by using relevant small geographical sectors. Although these places are routinely defined according to administrative boundaries on the basis of statistical criteria, it is important to carefully consider the way they are circumscribed as they can create spatial analysis problems and produce misleading information. This article introduces a new approach to defining neighbourhood units which is based on the integration of elements stemming from the socioeconomic situation of the area, its history, and how it is perceived by local key actors. RESULTS: Using this set of geographical units shows important socioeconomic and health disparities at the local scale. These disparities can be seen, for example, in a 16-year difference in disability-free life expectancy at birth, and a $10,000-difference in average personal income between close neighbourhoods. The geographical units also facilitate information transfer to local stakeholders. CONCLUSION: The context of this study has made it possible to explore several relevant methodological issues related to the definition of neighbourhood units. This multi-perspective approach allows the combination of many different elements such as physical structures, historical and administrative boundaries, material and social deprivation of the population, and sense of belonging. Results made sense to local stakeholders and helped them to raise important issues to improve future developments.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".