Intensive care unit clinicians identify many barriers to, and facilitators of, early mobilisation: a qualitative study using the Theoretical Domains Framework
Bibliographic record
Abstract
QUESTION: From the perspective of intensive care unit (ICU) clinicians, what are the barriers to and facilitators of implementing early mobilisation? DESIGN: A qualitative study using focus groups, with analysis using the Theoretical Domains Framework. PARTICIPANTS: Physicians, nurses, respiratory therapists and physiotherapists from the ICUs of three university-affiliated hospitals in Montreal, Canada. METHODS: Four focus group meetings were conducted with 33 participating ICU clinicians. Two researchers independently performed thematic content analysis on verbatim transcriptions of the audio recordings using the Theoretical Domains Framework. RESULTS: Data saturation was reached after the third focus group. Thirty-six barriers were categorised in 13 domains of the Theoretical Domains Framework. The key barriers to early mobilisation were: lack of conviction and knowledge regarding the available evidence about early mobilisation; lack of attention to the provision of optimal care; poor communication; the unpredictable nature of the ICU; and limited staffing, equipment, time and clinical knowledge. Twenty-five facilitators categorised in ten TDF domains were also identified. These included individual-level facilitators (intrinsic motivation, positive outcome expectations, conscious effort to mobilise early, good planning/coordination, the presence of ICU champions, and expert support by a physiotherapist) and organisational-level facilitators (reminder system, pro-early mobilisation culture, implementation of an early mobilisation protocol, and improved ICU organisation). CONCLUSIONS: A broad array of barriers to and facilitators of early mobilisation in the ICU were identified in this study. Clinicians can consider whether these barriers and facilitators are operating in their ICU. These may inform the design of tailored knowledge translation interventions to promote early mobilisation in the ICU.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".