Are residents of downtown Toronto influenced by their urban neighbourhoods? Using concept mapping to examine neighbourhood characteristics and their perceived impact on self-rated mental well-being
Bibliographic record
Abstract
BACKGROUND: There is ample evidence that residential neighbourhoods can influence mental well-being (MWB), with most studies relying on census or similar data to characterize communities. Few studies have actively investigated local residents' perceptions. METHODS: Concept mapping was conducted with residents from five Toronto neighbourhoods representing low income and non-low income socio-economic groups. These residents participated in small groups and attended two sessions per neighbourhood. The first session (brainstorming) generated neighbourhood characteristics that residents felt influenced their MWB. A few weeks later, participants returned to sort these neighbourhood characteristics and rate their relative importance in affecting residents' 'good' and 'poor' MWB. The data from the sorting and rating groups were analyzed to generate conceptual maps of neighbourhood characteristics that influence MWB. RESULTS: While agreement existed on factors influencing poor MWB (regardless of neighbourhood, income, gender and age), perceptions related to factors affecting good MWB were more varied. For example, women were more likely to rank physical beauty of their neighbourhood and range of services available as more important to good MWB, while men were more likely to cite free access to computers/internet and neighbourhood reputation as important. Low-income residents emphasized aesthetic attributes and public transportation as important to good MWB, while non-low-income residents rated crime, negative neighbourhood environment and social concerns as more important contributors to good MWB. CONCLUSION: These findings contribute to the emerging literature on neighbourhoods and MWB, and inform urban planning in a Canadian context.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".