Barriers and facilitators encountered by healthcare providers when supporting individuals with SCI to change their bowel care
Bibliographic record
Abstract
BACKGROUND: Individuals with spinal cord injury (SCI) are dissatisfied with their bowel care, but 71% have not changed their care for at least 5 years. Recently, individuals with SCI expressed a need for knowledge about bowel care options. Healthcare providers (HCP) play a crucial role in supporting bowel care changes OBJECTIVE: We aimed to understand the barriers and facilitators HCP face when discussing changes in bowel care with individuals with SCI. METHODS: Semi-structured interviews were conducted with HCP in partnership with Spinal Cord Injury British Columbia and key community stakeholders. Barriers and facilitators were extracted, deductively coded using the Theoretical Domains Framework, then inductively analysed for themes. RESULTS: Themes highlighted that effective bowel care requires diverse knowledge from a multidisciplinary team. Lack of time to prioritise bowel care and limited healthcare resources were barriers to improving care, which may be augmented through regular bowel care review of both medical and person-centered priorities. Facilitators were accessible and tailored knowledge sharing of care options, complemented by peer support. CONCLUSION: This study highlights the need for targeted interventions that reduce barriers and enhance facilitators to changing care routines, supporting individuals with SCI to change bowel care when needed, and improving quality of life.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".