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Record W3021123924 · doi:10.1186/s12913-020-05270-x

Selecting and tailoring implementation interventions: a concept mapping approach

2020· article· en· W3021123924 on OpenAlex
Elaine Yuen Ling Kwok, Sheila Moodie, Barbara Jane Cunningham, Janis Oram Cardy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBMC Health Services Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsKnowledge translationHealth informaticsStakeholderFocus groupKnowledge managementNursing researchComputer sciencePsychological interventionMedicineProcess managementPublic healthNursingPublic relationsBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: To improve the uptake of research into practice, knowledge translation frameworks recommend tailoring implementation strategies to address practice barriers. This study reports our experience pairing the Theoretical Domains Framework with information from multiple stakeholder groups to co-develop practice-informed strategies for improving the implementation of an evidence-based outcome measurement tool across a large community health system for preschoolers with communication impairments. METHODS: Concept mapping was used to identify strategies for improving implementation of the Focus on the Outcomes of Communication Under Six (FOCUS) in Ontario Canada's Preschool Speech and Language Program. This work was done in five stages. First, we interviewed 37 speech-language pathologists (clinicians) who identified 90 unique strategies to resolve practice barriers to FOCUS implementation. Second, clinicians (n = 34), policy-makers (n = 3), and members of the FOCUS research team (n = 6) sorted and rated the strategies by importance and feasibility. Third, stakeholders' sorting data were analyzed to generate a two-dimensional concept map. Based on the rating data from stakeholders, we prioritized a list of strategies that were rated as highly important and highly feasible, and summarized the practice barriers addressed by each of the prioritized strategies. Fourth, we validated these findings with stakeholders via an online survey. Fifth, the mechanisms of action of the prioritized list of strategies were considered based on available evidence from the Theoretical Domains Framework and associated behavior change literature. RESULTS: Stakeholders categorized the 90 unique implementation strategies into a six-cluster concept map. Based on stakeholders' ratings, a list of 14 implementation strategies were prioritized. These implementation strategies were reported to resolve barriers within the environmental context and resources and beliefs about consequences domains of the Theoretical Domains Framework. All but one of the prioritized strategies have a demonstrated link in resolving existing barriers according to the behavioral change literature. CONCLUSIONS: Our study contributes to a growing literature that demonstrates the process of tailoring implementation strategies to specific barriers. Practical drawbacks and benefits of using concept mapping as a way to engage stakeholders in implementation research are discussed.

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.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.797
GPT teacher head0.726
Teacher spread0.071 · 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