Neighbourhood deprivation and adolescent self-esteem: Exploration of the ‘socio-economic equalisation in youth’ hypothesis in Britain and Canada
Bibliographic record
Abstract
Material deprivation is an important determinant of health inequalities in adults but there remains debate about the extent of its importance for adolescent wellbeing. Research has found limited evidence for an association between adolescent health and socio-economic status, leading authors to suggest that there is an 'equalisation' of health across socio-economic groups during the adolescent stage of the life-course. This paper explores this 'equalisation' hypothesis for adolescent psychological wellbeing from a geographical perspective by investigating associations between neighbourhood deprivation and self-esteem in Britain and Canada. Data from the British Youth Panel (BYP) and the National Longitudinal Survey of Children and Youth (NLSCY) on adolescents aged 11-15 for the time period 1994-2004 were used to estimate variations in low self-esteem between neighbourhoods using multilevel logistic regression. Models were extended to estimate associations between self-esteem and neighbourhood deprivation before and after adjustment for individual and family level covariates. Moderation by age, sex, urban/rural status, household income and family structure was investigated. There were no significant differences in self-esteem between the most deprived and most affluent neighbourhoods (Canada unadjusted OR = 1.00, 95% CI 0.76, 1.33; Britain unadjusted OR = 1.25, 95% CI 0.74, 2.13). The prevalence of low self-esteem was higher (in Canada) for boys in the least deprived neighbourhoods compared to other neighbourhoods. No other interactions were observed. The results presented here offer some (limited) support for the socio-economic equalisation in youth hypothesis from a geographical perspective: with specific reference to equalisation of the relationship between neighbourhood deprivation and self-esteem and psychological health in early adolescence. This contrasts with previous research in the United States but supports related work from Britain. The lack of interactions with key social and economic variables suggests that findings might apply across a range of family circumstances and different communities in Britain and Canada. Policy implications are discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 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".