MétaCan
Menu
Back to cohort
Record W7027395025

Changing behaviour, ‘more or less’: Investigating
\nwhether there is a basis for designing different
\ninterventions for implementation and deimplementation.

2016· dissertation· en· W7027395025 on OpenAlexaboutno aff

Bibliographic record

VenueCity Research Online (City University London) · 2016
Typedissertation
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionHealth careBehaviour changeProcess (computing)Variety (cybernetics)Systematic reviewExploratory researchTriangulationWork (physics)Behavior change
DOInot available

Abstract

fetched live from OpenAlex

Background: The process of decreasing ineffective or harmful healthcare (deimplementation) may require different approaches than those used to promote uptake of new procedures (implementation) but research into different approaches is currently lacking. It has not been determined if there is a theoretical and evidence-based rationale for designing different interventions for implementation and de-implementation.
\n
\nObjectives: The objectives of this thesis were to: 1) Investigate whether there is a theoretical basis for identifying different mechanisms of change by which behaviour might increase versus decrease; 2) Assess whether predictors of health professional behaviour differ depending on whether behaviour was one clinicians should implement or behaviours clinicians should de-implement; and 3) Identify the Behaviour Change Techniques of published implementation and de-implementation interventions to determine if there is a difference between the techniques reported in these interventions.
\n
\nMethods: Study 1 used Critical Interpretive Synthesis to investigate whether a theoretical rationale exists for identifying different mechanisms of change by which interventions may work for implementation and de-implementation. Study 2 investigated whether the theoretical constructs commonly used to predict health professional behaviour differ based on whether the behaviours should be implemented or de-implemented. It was an exploratory study involving secondary analysis on 13 existing questionnaire datasets from a variety of healthcare professional groups in primary care settings in the United Kingdom and Canada. Study 3 involved the classification and frequency of Behaviour Change Techniques in implementation and de-implementation interventions from selected Cochrane Effective Practice and Organisation of Care systematic reviews. Findings from these three studies were interpreted using the concurrent triangulation approach to report on the key findings.
\n
\nResults: Operant Learning Theory (OLT) proposes different approaches to increasing and decreasing behaviour changes and therefore implementation and de-implementation interventions (Studies 1 & 3), despite a number of commonly used theories being poor predictors of behaviours for implementation and de-implementation (Study 2). Additionally, whilst the range of techniques was limited, the technique Behaviour substitution was frequently used to decrease health professionals’ behaviours (Study 3) and also identified as a strategy commonly used to decrease behaviour in general (Study 1).
\n
\nConclusion: Whilst the findings suggest that OLT may be promising in developing different interventions for implementation and de-implementation, how best to use these OLT principles is unclear. In instances whereby Behaviour substitution is part of a de-implementation intervention, it is not clear how best to identify the substitute behaviour. Additional investigation is required to better inform the design of implementation and de-implementation interventions.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0060.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.600
GPT teacher head0.638
Teacher spread0.037 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCity Research Online (City University London)Same topicHealth Policy Implementation ScienceFrench-language works237,207