The impact of working conditions on the health of taxi drivers in an urban metropolis
Bibliographic record
Abstract
Purpose This descriptive qualitative study explores how working conditions impact the health of taxi drivers in Toronto, Canada. Design/methodology/approach Drivers were recruited between September 2016 and March 2017. A total of 14 semi-structured qualitative interviews and one focus group (n = 11) were conducted. Transcripts were analyzed inductively through a socioecological lens. Findings The findings of this study are as follows: drivers acknowledged that job precariousness (represented by unstable employment, long hours and low wages) and challenging workplace conditions (sitting all day and limited breaks) contribute to poor physical/mental health. Also, these conditions undermine opportunities to engage in health-protective behaviors (healthy eating, regularly exercising and taking breaks). Drivers do not receive health-enabling reinforcements from religious/cultural networks, colleagues or their taxi brokerage. Drivers do seek support from their primary care providers and family for their physical health but remain discreet about their mental health. Research limitations/implications As this study relied on a convenience sample, the sample did not represent all Toronto taxi drivers. All interviews were completed in English and all drivers were male, thus limiting commentary on other experiences and any gender differences in health management approaches among drivers. Practical implications Given the global ubiquity of taxi driving and an evolving workplace environment characterized by growing competition, findings are generalizable across settings and may resonate with other precarious professions, including long-haul truck operators and Uber/Lyft drivers. Findings also expose areas for targeted intervention outside the workplace setting. Originality/value Health management among taxi drivers is understudied. A fulsome, socioecological understanding of how working conditions (both within and outside the workplace) impact their health is essential in developing targeted interventions to improve health outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".