Understanding patient barriers and enablers to accessing community resources: a qualitative study to inform navigation service delivery
Bibliographic record
Abstract
BACKGROUND: Individuals face multifaceted barriers to accessing community resources, which can significantly impact their ability to obtain necessary support and services. This study examines barriers and enablers to access community resources among primary care patients and explores the interplay between individual- and system-level factors that influence access to resources. METHODS: In this qualitative study, we conducted 32 semi-structured interviews with primary care patients who participated in a social prescribing trial that compared two navigation services: ARC (patient-centered longitudinal support) vs. Ontario-211 (free 24-hour helpline service) in two regions, Ottawa and Sudbury, in the province of Ontario, Canada. We conducted thematic data analysis with a deductive/inductive hybrid approach, employing Levesque's theoretical framework for access to health that examines the various system and individual level factors influencing access to healthcare services. FINDINGS: At the system level, various systemic barriers encompassing broader organisational, structural, and policy-related elements influenced patients' access to community resources (outreach, availability, location, costs and accommodations for patient needs and preferences). These factors directly interacted with various patient-level factors (awareness of needs, ability to seek support, to reach resources and to pay for those, their physical and mental health, motivation, and confidence to engage in care) to determine access. CONCLUSIONS: Our findings highlight individual- and system-level barriers and enablers for accessing community resources among socially complex primary care patients with multiple unmet needs. As governments and organizations in Canada are increasingly investing in community-based services to address adverse social determinants of health (SDH), an upstream approach that reduces both systemic and individual-level barriers to access is warranted. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT03451552 registration date 15th Dec 2017.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".