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Record W2953016251 · doi:10.1186/s12913-019-4226-7

Measurement without management: qualitative evaluation of a voluntary audit & feedback intervention for primary care teams

2019· article· en· W2953016251 on OpenAlexafffundabout
Daniel Wagner, Janet Durbin, Jan Barnsley, Noah Ivers

Bibliographic record

VenueBMC Health Services Research · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsWomen's College HospitalUniversity of TorontoCentre for Addiction and Mental HealthUniversity of Calgary
FundersCanadian Institutes of Health ResearchUniversity of TorontoOntario Ministry of Health and Long-Term Care
KeywordsNursing researchAuditContext (archaeology)MedicineHealth administrationProcess managementQuality managementNursingHealth informaticsHealth services researchData collectionIntervention (counseling)Resource (disambiguation)Medical educationPublic healthComputer scienceOperations managementManagement systemBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: The use of clinical performance feedback to support quality improvement (QI) activities is based on the sound rationale that measurement is necessary to improve quality of care. However, concerns persist about the reliability of this strategy, known as Audit and Feedback (A&F) to support QI. If successfully implemented, A&F should reflect an iterative, self-regulating QI process. Whether and how real-world A&F initiatives result in this type of feedback loop are scarcely reported. This study aimed to identify barriers or facilitators to implementation in a team-based primary care context. METHODS: Semi-structured interviews were conducted with key informants from team-based primary care practices in Ontario, Canada. At the time of data collection, practices could have received up to three iterations of the voluntary A&F initiative. Interviews explored whether, how, and why practices used the feedback to guide their QI activities. The Consolidated Framework for Implementation Research was used to code transcripts and the resulting frameworks were analyzed inductively to generate key themes. RESULTS: Twenty-five individuals representing 18 primary care teams participated in the study. Analysis of how the A&F intervention was used revealed that implementation reflected an incomplete feedback loop. Participation was facilitated by the reliance on an external resource to facilitate the practice audit. The frequency of feedback, concerns with data validity, the design of the feedback report, the resource requirements to participate, and the team relationship were all identified as barriers to implementation of A&F. CONCLUSIONS: The implementation of a real-world, voluntary A&F initiative did not lead to desired QI activities despite substantial investments in performance measurement. In small primary care teams, it may take long periods of time to develop capacity for QI and future evaluations may reveal shifts in the implementation state of the initiative. Findings from the present study demonstrate that the potential mechanism of action of A&F may be deceptively clear; in practice, moving from measurement to action can be complex.

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)0.071
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0710.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.634
GPT teacher head0.710
Teacher spread0.076 · 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

Citations35
Published2019
Admission routes3
Has abstractyes

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