Perspectives of early mobility in the cardiac surgery intensive care unit: an interpretive description study
Bibliographic record
Abstract
ObjectiveEarly mobility after cardiac surgery in the intensive care unit improves patient outcomes, yet implementation by clinicians remains inconsistent. To inform interventions to increase adoption, this study sought to explore clinicians' perspectives on the definition of early mobility in the cardiac surgery intensive care unit and describe perceived barriers and facilitators to implementation.DesignThis qualitative study was guided by interpretive description.SettingA tertiary hospital that performs 1000 cardiac surgeries annually.ParticipantsA total of 26 clinicians, including nurses, physicians, healthcare aides, respiratory therapists, and physiotherapists, participated.Main MeasuresThe nine focus groups and four individual interviews were audio-recorded, transcribed verbatim, and analyzed using thematic analysis.ResultsThree themes and 13 categories were created from the data: (1) Incongruent operationalization and conceptualization (across micro, meso, and macro levels), (2) uncertainty, and (3) inconsistency in mobilization practices. Themes were rooted in ambiguous definitions of "early" and "mobility," discrepancy between conceptualization and implementation of timing, varied interpretations of success, and perceived safety concerns. In addition, 4 barriers and 4 facilitators were identified and categorized into patient-care and clinician-related.ConclusionDeveloping early mobility protocols and education informed by clinicians may enhance its implementation in the cardiac surgery intensive care unit. Incorporating the perspectives of clinicians into early mobility protocol development is essential to support behaviour change and provide this beneficial care.
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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 | 0.035 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".