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Record W7161806158 · doi:10.82308/20948

Neighbourhoods, stress and distress

2004· dissertation· en· W7161806158 on OpenAlexaboutno aff
Saeeda S. Khan

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsDistressNeighbourhood (mathematics)Logistic regressionStress testStress (linguistics)Metropolitan areaMultilevel modelPsychological distress

Abstract

fetched live from OpenAlex

This study examines stress and distress experienced by working age individuals in the urban environment. The goals of this research are twofold: (1) to test for a social gradient in stress and distress; and (2) to test for environmental effects on the reporting of stress and distress, specifically focussing on variations in stress and distress across neighbourhoods in Montreal. Montreal was selected as the focus of this study because it is a large metropolitan region with some of the highest income disparities in Canada. Individual-level logistic regression models and multilevel analyses of the 2000/01 Canadian Community Health Survey were applied to identify the determinants of stress and distress and to determine the degree of variation in stress (n = 1944) and distress (n = 1836) captured at the neighbourhood level. Results showed that a social gradient exists with distress in Montreal, but not stress, and that neighbourhoods have an effect on distress above and beyond individual characteristics.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.331

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.347
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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