The promises and perils of a free rural inter-city transportation scheme: A mixed-methods study from Northern Saskatchewan
Bibliographic record
Abstract
OBJECTIVE: Transportation is a critical health determinant, yet the last decade has witnessed rapid disinvestment across Canada (particularly in rural contexts) with negative health consequences. We sought to explore and describe the benefits and challenges faced in operating the first community-driven free-transportation scheme in Saskatchewan that emerged in response to widespread unavailability of public transportation due to budget cuts (austerity). METHODS: We conducted a mixed-methods community-based participatory research study involving 22 interviews with bus riders and service administrators. We also performed descriptive statistics and chi-squared analyses on bus rider data (data on 1185 trips routinely collected between July 2023 and December 2023) to explore sociodemographic characteristics and trip purposes of bus riders. RESULTS: All trips were completed by 616 community members using the free bus service between July 2023 and December 2023. Community members took an average of 5 trips (median = 2.0) with a maximum of 22 trips being taken by one community member (1.9% of all trips). Most trips were by women (53%), and older adults mostly used the free bus for medical purposes (22% of riders were older adults and 34% of these used the bus for medical reasons). Qualitatively, the bus service has increased access to care and promotes social participation and autonomy, especially for older adults. The service however faces some challenges, including funding disruptions and difficulty recruiting and retaining drivers. CONCLUSION: Free inter-community transportation (i.e. transportation across cities and municipalities) promotes health equity and access. In contexts without access to public transportation, governments could support community-driven initiatives through increased funding.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".