MétaCan
Menu
← Back to cohort
Record W4415007493 · doi:10.1101/2025.10.08.25336902

Development and Pilot testing of a Leadership Module to Support Quality Improvement Teams in Nursing Homes

2025· preprint· en· W4415007493 on OpenAlexaff
Liane Ginsburg, Whitney Berta, Carole A. Estabrooks, Matthias Hoben, Lonnie Rae Kehler, Don C. McLeod, Jennifer Pietracci, Danielle Saj, Georgina Veldhorst, Adrian Wagg, Malcolm Doupe

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of ManitobaWinnipeg Regional Health AuthorityUniversity of AlbertaUniversity of TorontoYork University
Fundersnot available
KeywordsCoachingPsychological interventionQuality managementTransformational leadershipQuality (philosophy)FidelityEmpirical researchProgram evaluationStakeholderCulture change

Abstract

fetched live from OpenAlex

ABSTRACT Background Leadership is a critical lever for supporting implementation of practice change ideas intended to improve care. We need evidence-based leadership programs to help front-line providers meaningfully implement practice change in complex care settings. Part of the SHIFT intervention, this paper describes and pilot tests a leadership program module (LeaderSHIFT) that provides training and implementation coaching to front-line leaders, as one of several integrated facilitated supports designed to help front-line care teams meaningfully enact practice change. Methods The LeaderSHIFT program module was developed based on empirical work, relevant facilitation and transformational leadership theories, and principles of stakeholder co-design and feasible engagement. A pilot implementation study was conducted that examined several of Proctor’s (2011) implementation outcomes. Results LeaderSHIFT includes four interactive workshops plus two one-on-one coaching sessions designed to develop capacity in four areas of implementation leadership: (1) Self-awareness, (2) Motivate and inspire, (3) Facilitate learning capacity, and (4 ) Support ‘team-oriented processes’. Pilot results suggest it can be successfully implemented (it was acceptable, adopted, appropriate, feasible). Fidelity (LeaderSHIFT role enactment) varied across pilot teams. Conclusions With a strong theoretical and empirical base, LeaderSHIFT highlights important, often overlooked, relational and socio-cultural aspects of successful implementation leadership. As such, the LeaderSHIFT program module has the potential to improve implementation of practice change interventions in nursing homes and other institutional care settings. Trial registration Registered at ClinicalTrials.gov (ID NCT03426072 ) on July 18, 2022. KEY MESSAGES REGARDING FEASIBILITY What uncertainties existed regarding the feasibility? While leadership is known to be a critical lever for implementation of evidence-informed practice change, there are few leadership training programs that have a relational focus designed to support broader team-based practice change initiatives; and uncertainty remains regarding implementability (feasibility, acceptability, appropriateness, fidelity) of this type of leadership module in complex care settings What are the key feasibility findings? The LeaderSHIFT module performed well on several key implementation outcomes (module acceptability, feasibility, appropriateness). Fidelity to implementation leadership was successful for managers who were able to enact relational aspects of the role. What are the implications of the feasibility findings for the design of the main study? Findings confirmed the value of one-to-one coaching for enhancing leaders’ relational competencies and prompted training overlap for senior and front-line leaders to ensure there is a common understanding of respective roles in intervention implementation.

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)0.011
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.197
GPT teacher head0.441
Teacher spread0.244 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venuemedRxiv→Same topicGeriatric Care and Nursing Homes→French-language works237,207→