Coping with a Lower Limb Amputation due to Vascular Disease in the Hospital, Rehabilitation, and Home Setting
Bibliographic record
Abstract
Objectives. To explore the coping strategies used following a lower limb amputation and their relationship to adjustment in the hospital, rehabilitation, and home setting. Method. Sixteen individuals who had a lower limb amputation due to vascular disease completed questionnaires, including the Ways of Coping Questionnaire (WCQ), during hospitalization (T1), at the end of rehabilitation (T2), and 2-3 months after discharge from rehabilitation (T3). A subsample ( n=10 ) also participated in three semistructured interviews analyzed using the approach of Miles and Huberman. Results. Self-controlling was the coping strategy used most, followed by seeking social support and positive reappraisal. Three additional coping strategies not found in the WCQ were identified in the qualitative data: noticing progress, learning new things, and using humor. Confrontive coping (T1) and escape-avoidance (T1, T2, and T3) were related to adjustment problems while positive reappraisal (T1 and T3), seeking social support (T1 and T3), and planful problem solving (T3) were associated with positive adjustment. Conclusion. Coping strategies used to deal with the amputation seem to vary across settings, thus signifying the complexity of the coping process following a lower limb amputation due to vascular disease.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".