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Record W3016531453 · doi:10.1016/j.jphys.2020.03.001

Intensive care unit clinicians identify many barriers to, and facilitators of, early mobilisation: a qualitative study using the Theoretical Domains Framework

2020· article· en· W3016531453 on OpenAlexafffundabout
David Anekwe, Siobhan C. Milner, André Bussières, Michel de Marchie, Jadranka Spahija

Bibliographic record

VenueJournal of physiotherapy · 2020
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsCentre Intégré de Santé et de Services Sociaux des LaurentidesJewish General HospitalCentre intégré de santé et de services sociaux de Chaudière-AppalachesJewish Rehabilitation HospitalMcGill UniversityUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre for Interdisciplinary Research in RehabilitationHôpital du Sacré-Cœur de Montréal
FundersMcGill University
KeywordsThematic analysisFocus groupMedicineKnowledge translationStaffingQualitative researchNursingPsychological interventionIntensive care unitContent analysisProtocol (science)Medical educationAlternative medicineKnowledge managementIntensive care medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.028
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.040
GPT teacher head0.428
Teacher spread0.389 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations36
Published2020
Admission routes3
Has abstractyes

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