Rural citizen-patient priorities for healthcare in British Columbia, Canada: findings from a mixed methods study
Bibliographic record
Abstract
BACKGROUND: The challenge of including citizen-patient voices in healthcare planning is exacerbated in rural communities by regional variation in priorities and a historical lack of attention to rural healthcare needs. This paper aims to address this deficit by presenting findings from a mixed methods study to understand rural patient and community priorities for healthcare. METHODS: We conducted a provincial survey of rural citizens-patients across British Columbia, Canada to understand their most pressing healthcare needs, supplemented by semi-structured interviews. Survey and interview participants were asked to articulate, in their own words, their communities' most pressing healthcare needs, to explain the importance of these priorities to their communities, and to offer possible solutions to address these challenges. Open-text survey responses and interview data were analyzed thematically to elicit priorities of the data and their significance to answer the research questions. RESULTS: We received 1,287 survey responses from rural citizens-patients across BC, 1,158 of which were considered complete. We conducted nine telephone interviews with rural citizens-patients. Participants stressed the importance of local access to care, including emergency services, maternity care, seniors care, specialist services and mental health and substance use care. A lack of access to primary care services was the most pronounced gap. Inadequate local health services presented geographic, financial and social barriers to accessing care, led to feelings of vulnerability among rural patients, resulted in treatment avoidance, and deterred community growth. CONCLUSIONS: Two essential prongs of an integration framework for the inclusion of citizen-patient voices in healthcare planning include merging patient priorities with population needs and system-embedded accountability for the inclusion of patient and community priorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".