Understanding transitions in care for persons with limb loss: a qualitative study exploring health care providers’ perspectives
Bibliographic record
Abstract
PURPOSE: To explore health care providers' (HCP) experiences related to transitions in care from inpatient rehabilitation to the community for patients with limb loss. MATERIALS AND METHODS: A qualitative study was conducted using semi-structured interviews. Participants were eligible if they were HCPs currently working in amputation rehabilitation at a rehabilitation hospital in Ontario, Canada, with at least 1-year experience in this setting, and could speak and understand English. Data were analyzed thematically using the six-step process of the DEPICT model dynamic reading, engaged codebook development, participatory coding, inclusive reviewing and summarizing of categories, collaborative analyzing and translating. RESULTS: Fourteen HCPs from a variety of health care professions participated in this study. Five key themes describe participants' perspectives on the factors impacting patients' transition in care following limb loss. Specifically, participants emphasized patient preparedness, HCP follow-up, finances and funding, patient self-management skills, and psychosocial support as factors that could influence the transition in care. CONCLUSION: This study identified challenges to transitions in care for people with limb loss. Future research is needed to evaluate solutions to address these challenges in transitions in 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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".