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Record W4295905005 · doi:10.2196/40611

The Influence of Context on Implementation and Improvement: Protocol for a Mixed Methods, Secondary Analyses Study

2022· article· en· W4295905005 on OpenAlexaffvenueabout
Carole A. Estabrooks, Yuting Song, Ruth A. Anderson, Anna Beeber, Whitney Berta, Stephanie Chamberlain, Greta G. Cummings, Yinfei Duan, Leslie A. Hayduk, Matthias Hoben, Holly J. Lanham, Janelle Perez, Jing Wang, Peter Norton

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of TorontoUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsKnowledge translationContext (archaeology)Qualitative propertyKnowledge managementData collectionStakeholderHealth careQualitative researchAction researchPsychologyComputer scienceProcess managementBusinessPublic relations

Abstract

fetched live from OpenAlex

BACKGROUND: Caring for the well-being of older adults is one of the greatest challenges in modern societies. Improving the quality of care and life for older adults and the work lives of their care providers calls for effective knowledge translation of evidence-based best practices. OBJECTIVE: This study's purpose is to contribute to knowledge translation by better understanding the roles of organizational context (workplace environment) and facilitation (process or role) in implementation and improvement success. Our study has 2 goals: (1) to advance knowledge translation science by further developing and testing the Promoting Action on Research Implementation in Health Services framework (which outlines how implementation relies on the interplay of context, facilitation, and evidence) and (2) to advance research by optimizing implementation success via tailoring of modifiable elements of organizational context and facilitation. METHODS: This is secondary analyses of 15 years of longitudinal data from the Translating Research in Elder Care (TREC) program's multiple data sources. This research is ongoing in long-term care (LTC) homes in western Canada. TREC data include the following: 5 waves of survey collection, 2 clinical trials, and regular ongoing outcome data for LTC residents. We will use a sequential exploratory and confirmatory mixed methods design. We will analyze qualitative and quantitative data holdings in an iterative process: (1) comprehensive reanalysis of qualitative data to derive hypotheses, (2) quantitative modeling to test hypotheses, and (3) action cycles to further refine and integrate qualitative and quantitative analyses. The research team includes 4 stakeholder panels: (1) system decision- and policy makers, (2) care home managers, (3) direct care staff, and (4) a citizen engagement group of people living with dementia and family members of LTC residents. A fifth group is our panel of external scientific advisors. Each panel will engage periodically, providing their perspectives on project direction and findings. RESULTS: This study is funded by the Canadian Institutes of Health Research. Ethics approval was obtained from the University of Alberta (Pro00096541). The results of the secondary analyses are expected by the end of 2023. CONCLUSIONS: The project will advance knowledge translation science by deepening our understanding of the roles of context, the interactions between context and facilitation, and their influence on resident and staff quality outcomes. Importantly, findings will inform understanding of the mechanisms by which context and facilitation affect the success of implementation and offer insights into factors that influence the implementation success of interventions in nursing homes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40611.

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)0.152
metaresearch head score (Gemma)0.168
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.848
Threshold uncertainty score0.803

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1520.168
Meta-epidemiology (narrow)0.0070.005
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0080.010
Science and technology studies0.0090.005
Scholarly communication0.0060.005
Open science0.0050.005
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0690.017

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 head0.861
GPT teacher head0.845
Teacher spread0.015 · 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.

Study designQualitative
DomainMethods
GenreProtocol

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

Citations6
Published2022
Admission routes3
Has abstractyes

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