Social inequalities in health information seeking among young adults in Montreal
Bibliographic record
Abstract
Over their lifecourse, young adults develop different skills and preferences in relationship to the information sources they seek when having questions about health. Health information seeking behaviour (HISB) includes multiple, unequally accessed sources; yet most studies have focused on single sources and did not examine HISB's association with social inequalities. This study explores 'multiple-source' profiles and their association with socioeconomic characteristics. We analyzed cross-sectional data from the Interdisciplinary Study of Inequalities in Smoking involving 2093 young adults recruited in Montreal, Canada, in 2011-2012. We used latent class analysis to create profiles based on responses to questions regarding whether participants sought health professionals, family, friends or the Internet when having questions about health. Using multinomial logistic regression, we examined the associations between profiles and economic, social and cultural capital indicators: financial difficulties and transportation means, friend satisfaction and network size, and individual, mother's, and father's education. Five profiles were found: 'all sources' (42%), 'health professional centred' (29%), 'family only' (14%), 'Internet centred' (14%) and 'no sources' (2%). Participants with a larger social network and higher friend satisfaction were more likely to be in the 'all sources' group. Participants who experienced financial difficulties and completed college/university were less likely to be in the 'family only' group; those whose mother had completed college/university were more likely to be in this group. Our findings point to the importance of considering multiple sources to study HISB, especially when the capacity to seek multiple sources is unequally distributed. Scholars should acknowledge HISB's implications for health inequalities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".