Managing fatigue following spinal cord injury: A qualitative exploration
Bibliographic record
Abstract
PURPOSE: To identify, from the perspectives of people with spinal cord injury (SCI), (a) appropriate components of a fatigue management programme; and (b) important outcomes or indicators of success. METHOD: Collaborative, qualitative methodology comprising four focus groups undertaken simultaneously in Kelowna, Prince George, Vancouver and Victoria, British Columbia, Canada. Participants included a purposive sample of 21 men and women with complete and incomplete SCI of high and low tetraplegia and paraplegia. Two family members, two care-providing assistants and four occupational therapists provided additional information (total n=29). Interpretive data analysis identified common themes addressing each research question. RESULTS: Building on those strategies they perceived to facilitate coping with fatigue, the participants identified 10 components of a helpful fatigue management programme. Dimensions of 'successful' outcomes from such a programme reflected quality of life concerns: enabling people with SCI to do the things they value, enhancing their sense of control over their lives, reducing pain and helplessness, increasing motivation and enhancing relationships strained by fatigue. CONCLUSIONS: This study identifies many of the necessary elements of a fatigue management programme to meet the specific needs of people with SCI; and ascertains important indicators of a successful programme from the perspectives of those who must live with the outcomes.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".