Health equity via inclusive communications: self-censorship of marginalized health needs in qualitative research
Bibliographic record
Abstract
Based on a multi-phase study conducted on the World Health Organization’s (WHO) age-friendly cities program, healthy-cities program, and global sustainable-cities initiatives, this paper assesses how older adults – particularly those living with chronic health conditions and disabilities – discuss mobility challenges in qualitative research. Initial results indicate that demobilizing levels of travel cost and travel risk are implied, with older adults refraining from direct discussion. Reflective analysis is performed on marginalized older adults’ conceptual associations, ideologies, and response to social norms, to identify sources of pressure which may lead to downplaying or self-censorship on transport disadvantage. Results highlight that inequalities may arise from both the built environment and how research trainees communicate in research. Despite literature which suggests severe impacts of transport disadvantage, older adults may face difficulties in voicing their needs when research trainees adhere to the following social norms. First, physical access and feasibility may be minimized as mere matters of convenience to the able-bodied individual; second, overcoming transportation barriers may be seen as based on one’s effort – as opposed to the disabling effects of the built environment; third, health and fitness in general may be also associated with one’s effort, and increasingly also to one’s commitment to environmental-sustainability; fourth, older adults may feel pressure to align perspectives with more privileged stakeholders, or with the general public; fifth, money and income may be delegitimized as apt research topics, regardless of how inability to overcome travel costs may impact daily-wellbeing. Implications for educating future gerontology-research trainees conclude the study.
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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 | 0.293 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.060 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.004 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".