Exploring the Decisional Needs of Patients living with Subacromial Pain Syndrome: a qualitative needs assessment study
Bibliographic record
Abstract
Abstract Background There are a variety of different treatments for patients living with subacromial pain syndrome (SAPS). All treatments have small to moderate effect sizes, and it is challenging when healthcare practitioners and patients need to decide on which treatment options to choose. The aim of this study was to explore and understand the decisional needs of patients with SAPS, to inform and support the decision-making process. Methods A qualitative research study, using semi-structured individual interviews with patients with SAPS. The interview guide was informed by the Ottawa Decision Support Framework (ODSF), previous research related to treatment decision-making, other decisional needs assessment studies, and inputs from patients with SAPS and healthcare practitioners. Data were analysed by using reflexive thematic text analysis and ODSF. The analysis was conducted in NVivo 12. Results We invited 22 participants of which 17 (age 22-71 years) took part in the study. We found three main themes related to individual decisional needs in the context of decision-making: 1) The necessity of certainty and adequate information as fundamental prerequisites for effective decision-making, 2) The importance of person-centred care to achieve a desirable decision, and 3) The need for a supportive environment to facilitate adaptation and acceptance of the decision. Conclusion The decision-making process faced by patients with SAPS is complex and involves several decisional needs. Our findings highlight the importance of healthcare professionals identifying and addressing patients’ decisional needs in consultations with patients with SAPS. Implications Our study provides guidance to clinical practice in terms of increased understanding of the decisional needs experienced by patients with SAPS. Furthermore, these results should inform future development of decision aids for patients with SAPS. Highlights Subacromial pain syndrome is a complex condition, demanding a thorough decision-making process We identified several concrete decisional needs through the Ottawa Decision Support Framework and further provided in-depth knowledge on key themes related to certainty, patient-centeredness, and support. Our study provides information of designing a decision aid supporting shared decision-making in future studies.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".