Communicating Standing and Walking Data after Spinal Cord Injury: A Patient-Engaged, Qualitative Study
Bibliographic record
Abstract
Background: The Standing and Walking Assessment Tool has been implemented by physical therapists across Canada, but there is no standardized communication tool to inform inpatients living with spinal cord injury (SCI) about their standing and walking ability. Objectives: To identify how inpatients with SCI are currently receiving feedback on their standing and walking ability, and to determine if and how they would like to receive information on their standing and walking. Methods: Ontario's Patient Engagement Framework informed study protocol development. Inpatients with SCI were recruited from a rehabilitation centre in Canada. Purposeful sampling considering severity of SCI and sex was adopted. Three to four months following discharge from inpatient rehabilitation, a semi-structured interview was conducted to explore participants'experiences and preferences regarding feedback on standing and walking ability during inpatient SCI rehabilitation. Interviews were audio-recorded and transcribed verbatim. A conventional content analysis was completed. Results: Fifteen individuals with SCI (5 female, 10 male) participated. Four themes emerged from the transcripts: (1) motivation for standing and walking, (2) current standing and walking practice, (3) participant preferences for feedback on standing and walking ability, and (4) perceptions of preexisting tools. Conclusion: Information on standing and walking ability was shared with inpatients with SCI in a variety of ways. Participants identified various preferences for the nature, format, and frequency of feedback concerning standing and walking ability during inpatient rehabilitation, which suggests the need for an individualized approach to communicating this information.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".