Patient Preparation in Walk-In Clinics: Qualitative Study on the Implementation of a Consultation Preparation Sheet. (Preprint)
Bibliographic record
Abstract
BACKGROUND Effective communication is essential for high-quality care, yet in walk-in clinics, patients often have limited time to prepare, and physicians face challenges in understanding patients’ needs due to lack of prior contact. To address this, a Consultation Preparation Sheet (CPS) was developed to help patients articulate their symptoms, concerns, and expectations. OBJECTIVE This study explored the implementation and perceived usefulness of a CPS designed to support patient engagement during walk-in clinic consultations. METHODS We conducted a qualitative exploratory study using semi-structured interviews and focus groups, guided by the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation and Maintenance) and the PACE method (Prepare, Ask, Check, Express). The CPS was implemented over nine months (January–October 2019) in a university-affiliated walk-in clinic in Quebec, Canada. During this period, 3,115 patients received the CPS in the waiting room. Of the 23 patients who expressed interest in further participation, 12 were recruited for interviews. Additional participants included 2 physicians, 2 administrative staff, 2 nursing assistants, and 1 clinic manager. In total, 6 interviews and 5 focus groups were conducted. Data were transcribed verbatim and thematically analyzed, with inter-rater validation supported by AI-assisted review. RESULTS Patients generally found the CPS helpful for organizing their thoughts, reducing stress, and avoiding omissions during consultations. It was considered particularly useful for patients with multiple concerns or communication difficulties. However, physicians rarely referred to the CPS, often preferring direct verbal exchanges. This lack of acknowledgment sometimes frustrated patients and diminished their perception of its usefulness. Organizational challenges—including inconsistent distribution, lack of explanation, and limited clinician engagement—further hindered implementation. Participants suggested clearer communication about the CPS’s purpose, stronger physician involvement, and the development of electronic formats accessible before visits to enhance its integration into clinical practice. CONCLUSIONS The CPS shows promise as a simple tool to foster patient readiness and engagement in walk-in consultations. However, its effectiveness depends on clinician acknowledgment and organizational support. Improving implementation strategies and testing digital adaptations may strengthen patient-clinician partnerships and enhance the patient experience in primary care.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".