A qualitative exploration of perceived barriers, facilitators and preferences of early mobilisation after aneurysmal subarachnoid haemorrhage
Bibliographic record
Abstract
PURPOSE: To determine the barriers, enablers and preferences for engagement in early mobilisation from the perspective of aneurysmal subarachnoid haemorrhage (aSAH) survivors in acute and intensive care settings. MATERIALS AND METHODS: A descriptive qualitative study using semi-structured interviews was conducted, audio recorded and transcribed. Thematic analysis was conducted to identify participant's preferences and perceived barriers and enablers to participation in early mobilisation using the Capability, Opportunity and Motivation - Behaviour (COM-B) model as a guiding framework. RESULTS: Thirteen aSAH survivors were interviewed; mean (SD) age was 53 (12) years, 70% were females. Enablers to mobilisation included mobility independence, knowledge of exercise benefits, self-confidence, self-motivation to recover, staff encouragement, toileting, staff and equipment availability and family visits. The key barriers identified were fatigue and headache, difficulty processing events, lack of information, hospital environment constraints, medical monitoring requirements, feeling conscious of other patients and concerns regarding potential risks. Four preference themes were identified which included increased opportunities to exercise, desire for information, social connections and adjunct therapies. CONCLUSIONS: This in-depth exploration has identified patient preferences, barriers and enablers that influence participation in early mobilisation. Findings will inform the design of early mobilisation programs that better meet the needs of patients following aSAH.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".