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Record W2617900652 · doi:10.1186/s13063-017-1965-9

Barriers and facilitators to healthcare professional behaviour change in clinical trials using the Theoretical Domains Framework: a case study of a trial of individualized temperature-reduced haemodialysis

2017· article· en· W2617900652 on OpenAlexafffundabout
Justin Presseau, Brittany Mutsaers, Ahmed A. Al‐Jaishi, Janet E. Squires, Christopher W. McIntyre, Amit Garg, Manish M. Sood, Jeremy Grimshaw

Bibliographic record

VenueTrials · 2017
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsWestern UniversityMcMaster UniversityOttawa Public HealthUniversity of OttawaImpactOttawa Hospital
FundersCanadian Institutes of Health ResearchKidney Foundation of CanadaDialysis ClinicsHeart and Stroke Foundation of Canada
KeywordsMedicineThematic analysisOptimismNursingHealth careDialysisFamily medicineRandomized controlled trialClinical trialCluster randomised controlled trialQualitative researchPsychologySocial psychologyIntervention (counseling)Surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Implementing the treatment arm of a clinical trial often requires changes to healthcare practices. Barriers to such changes may undermine the delivery of the treatment making it more likely that the trial will demonstrate no treatment effect. The 'Major outcomes with personalized dialysate temperature' (MyTEMP) is a cluster-randomised trial to be conducted in 84 haemodialysis centres across Ontario, Canada to investigate whether there is a difference in major outcomes with an individualized dialysis temperature (IDT) of 0.5 °C below a patient's body temperature measured at the beginning of each haemodialysis session, compared to a standard dialysis temperature of 36.5 °C. To inform how to deploy the IDT across many haemodialysis centres, we assessed haemodialysis physicians' and nurses' perceived barriers and enablers to IDT use. METHODS: We developed two topic guides using the Theoretical Domains Framework (TDF) to assess perceived barriers and enablers to IDT ordering and IDT setting (physician and nurse behaviours, respectively). We recruited a purposive sample of haemodialysis physicians and nurses from across Ontario and conducted in-person or telephone interviews. We used directed content analysis to double-code transcribed utterances into TDF domains, and inductive thematic analysis to develop themes. RESULTS: We interviewed nine physicians and nine nurses from 11 Ontario haemodialysis centres. We identified seven themes of potential barriers and facilitators to implementing IDTs: (1) awareness of clinical guidelines and how IDT fits with local policies (knowledge; goals), (2) benefits and motivation to use IDT (beliefs about consequences; optimism; reinforcement; intention; goals), (3) alignment of IDTs with usual practice and roles (social/professional role and identity; nature of the behaviour; beliefs about capabilities), (4) thermometer availability/accuracy and dialysis machine characteristics (environmental context and resources), (5) impact on workload (beliefs about consequences; beliefs about capabilities), (6) patient comfort (behavioural regulation; beliefs about consequences; emotion), and (7) forgetting to prescribe or set IDT (memory, attention, decision making processes; emotion). CONCLUSIONS: There are anticipatable barriers to changing healthcare professionals' behaviours to effectively deliver an intervention within a randomised clinical trial. A behaviour change framework can help to systematically identify such barriers to inform better delivery and evaluation of the treatment, therefore potentially increasing the fidelity of the intervention to increase the internal validity of the trial. These findings will be used to optimise the delivery of IDT in the MyTEMP trial and demonstrate how this approach can be used to plan intervention delivery in other clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT02628366 . Registered November 16 2015.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.043
metaresearch head score (Gemma)0.047
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0430.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.315
GPT teacher head0.556
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

Study designRandomized trial
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".

Quick stats

Citations71
Published2017
Admission routes3
Has abstractyes

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