Patient Experience in Accessing Community Resources – A Qualitative Study
Bibliographic record
Abstract
Context: Community resources are often required in addition to primary care to effectively address a variety of health and social needs. However, access to these resources is influenced by various system and individual level factors. Little is known about the factors that enhance and prevent access to community resources. Objective: To investigate the facilitators and barriers to accessing community resources. Study Design, Intervention, Setting, and Population: A randomized control trial of 326 primary care patients compared the Access to Resources in the Community (ARC) navigation model to the Ontario 211 Community and Social Services Help line in supporting individuals’ access to community resources in Ottawa and Sudbury, Ontario (Canada). The patient experience was captured through 32 interviews (24 conducted in English and 8 in French) using a semi-structured approach. Purposeful sampling was used to maximize participant variability. Interviews were recorded, transcribed and analyzed applying a deductive approach with a uniquely developed coding scheme adapted from Levesque et al’s 2013 Access to Primary Health Care Framework. Data was coded in QSR NVivo® 9 by two independent coders to ensure reliability and consensus. This analysis focuses exclusively on the facilitators and barriers that patients reported experiencing and does not include the impact of the navigation services on these factors. Results: System level facilitators to access included resources making themselves known through outreach activities, provision of whole-person care, high quality of care, and good follow-up. Individual level facilitators involved patients9 autonomy in seeking resources, adequate social support, access to insurance, feeling empowered to access resources and high self-efficacy. System level barriers faced by patients included inadequate advertising of available resources, lack of cultural sensitivity, distance from resources, long wait times, and resources not meeting patient health and social needs. Individual level barriers included limited access and knowledge of technology, poor mental and physical health, lack of transportation, inability to pay, lack of adherence, and low motivation. Conclusion: Strategies to enhance facilitators and address barriers are required to achieve equitable access to community resources.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".