The Black Box of Patient Education: An Expert Consultation on Patient Education Interventions and Strategies for the Management of Subacromial Pain Syndrome
Bibliographic record
Abstract
Purpose: To identify patient education, interventions, and strategies to optimize the management of subacromial pain syndrome (SAPS) in physical therapy, based on the experiential knowledge of patient-partners and caregivers involved in the rehabilitation of this condition. Method: = 5) and an occupational therapist with extensive clinical experience, as well as a patient-partner. Analysis followed the Framework method. Results: Two main themes emerged: (1) interventions directly related to patient education, consisting of nine sub-themes, including symptom self-management and pain phenomenon, and (2) patient education strategies to broadly frame the interventions, consisting of 10 sub-themes, including educational materials and clinical teaching approaches. Conclusion: The consultation confirmed and expanded the knowledge from the literature by adding knowledge that emerged from the experts' practical experience. It resulted in the development of preliminary statements on structured patient education interventions and management strategies for SAPS. These emerging statements are, to our knowledge, the first to inform patient education specifically as it relates to the management of SAPS taking into account psychosocial and contextual factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".