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Record W4404296007 · doi:10.1136/bmjqs-2024-017795

‘We listened and supported and depended on each other’: a qualitative study of how leadership influences implementation of QI interventions

2024· article· en· W4404296007 on OpenAlexafffund
Liane Ginsburg, Adam Easterbrook, Ariane S. Massie, Whitney Berta, Lynda van Dreumel, Carole A. Estabrooks, Peter Norton, Adrian Wagg

Bibliographic record

VenueBMJ Quality & Safety · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of CalgaryUniversity of AlbertaUniversity of British ColumbiaUniversity of TorontoYork University
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionThematic analysisScope (computer science)MedicineHealth careNursingTransformational leadershipCoachingIntervention (counseling)Qualitative researchLeadership developmentMedical educationPublic relationsPsychologySociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: There is growing recognition in the literature of the 'Herculean' efforts required to bring about change in healthcare processes and systems. Leadership is recognised as a critical lever for implementation of quality improvement (QI) and other complex team-level interventions; however, the processes by which leaders facilitate change are not well understood. The aim of this study is to examine 'how' leadership influences implementation of QI interventions. METHODS: We drew on the leadership literature and used secondary data collected as part of a process evaluation of the Safer Care for Older Persons in residential Environments (SCOPE) QI intervention to gain insights regarding the processes by which leadership influences QI implementation. Specifically, using detailed process evaluation data from 31 unit-based nursing home teams we conducted a thematic analysis with a codebook developed a priori based on the existing literature to identify leadership processes. RESULTS: Effective leaders (ie, those who care teams felt supported by and who facilitated SCOPE implementation) successfully developed and reaffirmed teams' commitment to the SCOPE QI intervention (theme 1), facilitated learning capacity by fostering follower participation in SCOPE and empowering care aides to step into team leadership roles (theme 2) and actively supported team-oriented processes where they developed and nurtured relationships with their followers and supported them as they navigated relationships with other staff (theme 3). Together, these were the mechanisms by which care aides were brought on board with the intervention, stayed on board and, ultimately, transplanted the intervention into the facility. Building learning capacity and creating a culture of improvement are thought to be the overarching processes by which leadership facilitates implementation of complex interventions like SCOPE. CONCLUSIONS: Results highlight important, often overlooked, relational and sociocultural aspects of successful QI leadership in nursing homes that can guide the design, implementation and scaling of complex interventions and can guide future research.

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)0.038
metaresearch head score (Gemma)0.064
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 score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0180.021
Scholarly communication0.0060.008
Open science0.0040.008
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.402
GPT teacher head0.599
Teacher spread0.197 · 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".

Quick stats

Citations14
Published2024
Admission routes2
Has abstractyes

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