Rehabilitation, not injury or treatment details, dominate proximal humeral fracture patient concerns: a thematic analysis
Bibliographic record
Abstract
Background, objectives, and aims: To provide treatment using a patient-centered care model, the provider must understand the needs and wants of the patient and ensure the patient has access to appropriate and necessary health information. The objective of this study was to determine what information is most desired by proximal humeral fracture patients following their injury.Methods: This qualitative study enrolled patients aged 60 years or older presenting with a proximal humeral fracture. Semi-structured interviews were conducted within one-month of injury and at 6-months post-injury. The interviews were transcribed, coded and analyzed using thematic analysis.Results: Four themes (biomedical information, recovery, engagement opportunities and support available) emerged from the coded data. Within one-month post-injury, the most commonly identified themes were rehabilitation and support available. Six-months after the injury, the most commonly identified theme remained rehabilitation, while the second most frequently identified theme shifted to engagement opportunities. The biomedical information theme emerged infrequently at both interviews. Conclusions: Patient-centered care models for proximal humeral fracture patients could be improved by adapting to dynamic information concerns. While the effect of the injury on the patient’s rehabilitation remained the leading concern for the duration of the study period, secondary concerns did change over time. Providing germane information to patients at timely intervals supports patient-centered care, patient engagement and ultimately may improve patient 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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".