Patients’ expectations and experiences with primary care management: A qualitative study
Bibliographic record
Abstract
Context: Shoulder pain management is challenging for primary care clinicians considering that 40% of affected individuals remain symptomatic one year after initial consultation. Developing tailored knowledge translation interventions founded on evidence-based recommendations while also considering patients’ expectations could improve primary care management of adults living with shoulder pain. Objective: The aim of this qualitative study is to explore patients’ expectations and experiences of their primary care management for shoulder pain. Study design and Analysis: Using a qualitative study design, we conducted virtual individual interviews. Interviews were recorded, transcribed into verbatims, and an inductive thematic analysis was performed. Setting: Various primary care settings in the Province of Quebec, Canada. Population studied: Adults who consulted a primary care clinician (physician or physiotherapist) in the past year for shoulder pain. Instrument: A semi-structured interview guide included questions related to patients’ history of shoulder pain and related disability, motivation to consult, expectations about medical management and rehabilitation and experience with care consultations and provider interactions. Outcome measures: Using deductive thematic analysis, emergent themes related to participants’ expectations and experiences of primary care consultations for shoulder pain were identified. Results: We interviewed 13 participants. Four overarching themes were identified: 1) I can’t sleep because of my shoulder; 2) I need to know what is happening with my shoulder; 3) But& we need to really see what is going on to help me!; and 4) Please take some time with me so I can understand what to do!. More specifically, several participants reported waiting until they experienced high levels of pain before consulting since they had low expectations about the ability of their family physician to help them. Although some participants felt that their physician took the time to listen to their concerns, many were dissatisfied with the initial evaluation of their condition, or the explanation or education provided. Conclusions: One unexpected finding that emerged from this study was the delay between the onset of shoulder pain and when patients decided to consult their primary care clinician. Several participants reported that their expectations were not met, especially when it came to explanations or education provided.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".