Healthcare workers’ perspectives on a prescription phone program to meet the health equity needs of patients in the emergency department: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: People experiencing homelessness and marginalization face considerable barriers to accessing healthcare services. Increased reliance on technology within healthcare has exacerbated these inequities. We evaluated a hospital-based prescription phone program aimed to reduce digital health inequities and improve access to services among marginalized patients in Emergency Departments. We examined the perceived outcomes of the program and the contextual barriers and facilitators affecting outcomes. METHODS: We conducted a constructivist qualitative program evaluation at two urban, academic hospitals in Toronto, Ontario. We interviewed 12 healthcare workers about their perspectives on program implementation and outcomes and analyzed the interview data using reflexive thematic analysis. RESULTS: Our analyses generated five interrelated program outcomes: building trust with patients, facilitating independence in healthcare, bridging sectors of care, enabling equitable care for marginalized populations, and mitigating moral distress among healthcare workers. Participants expressed that phone provision is critical for adequately serving patients who face barriers to accessing health and social services, and for supporting healthcare workers who often lack resources to adequately serve these patients. We identified key contextual enablers and challenges that may influence program outcomes and future implementation efforts. CONCLUSIONS: Our findings suggest that providing phones to marginalized patient populations may address digital and social health inequities; however, building trusting relationships with patients, understanding the unique needs of these populations, and operating within a biopsychosocial model of health are key to program success.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".