“Please listen to me, I want to know what is wrong with my shoulder”: A qualitative study exploring patients’ expectations and experiences with primary care management.
Bibliographic record
Abstract
Abstract Background: The management of shoulder pain 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 for shoulder pain. The aim of this qualitative study is to explore patients’ expectations and experiences of their primary care consultation for shoulder pain. Methods: In this qualitative study, participants with shoulder pain and having consulted a primary care physician in the past year were interviewed. All the semi-structured interviews were transcribed into verbatims, and inductive thematic analysis was performed to identify themes related to the participants’ expectations and experiences of primary care consultations for shoulder pain. Results: Thirteen participants with shoulder pain were interviewed (8 women, 5 men; mean age 50 ± 12 years). Eleven of them initially consulted a family or an emergency physician, and two participants initially consulted a physiotherapist. Four overarching themes related to patients’ expectations and experiences were identified from our thematic analysis: 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!. Several participants waited until they experienced a high level of shoulder pain before making an appointment since they were not confident about what their family physician could do to manage their condition. Although some participants felt that their physician took the time to listened to their concerns, many were dissatisfied with the limited assessment and education provided by the clinician. Conclusions: Implementing evidence-based recommendations while also considering patients’ expectations is important as it may improve care delivery and patients’ satisfaction with care. Several participants reported that their expectations were not met, especially when it came to the explanations provided. 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.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".