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Record W2903146098 · doi:10.1136/bmjoq-2017-000259

Evaluation of the McMaster Family Health Team: results and practical implications for quality improvement

2018· article· en· W2903146098 on OpenAlexaffabout
Laila Nasser, Alix Stosic, David Price

Bibliographic record

VenueBMJ Open Quality · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsHamilton Health SciencesMcMaster University
Fundersnot available
KeywordsContext (archaeology)Relevance (law)Quality (philosophy)ConversationSet (abstract data type)Quality managementHealth carePerformance measurementProcess managementComputer scienceKnowledge managementMedicineOperations managementPsychologyBusinessMarketingEngineering

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the McMaster Family Health Team (MFHT) as part of a Continuous Quality Improvement initiative using a set of provincial performance metrics to demonstrate which measures of assessment are actually clinically meaningful in context and where system-level changes might be implemented to improve operational practice. METHODS: Measures were selected from the Primary Care Performance Measurement Framework based on data availability for the MFHT and provincial comparators. The measures explored in this paper are those that were deemed to have actionable properties. Data were extracted from billing reports, electronic medical records and information collated for the Association of Family Health Teams of Ontario Data to Decisions database. Metrics were then examined to demonstrate the importance of interpretation in clinical context. CONCLUSIONS: Quantitative assessment of performance based on standardised measures is a suitable starting point when evaluating a practice, however it is not appropriate as a stand-alone report card of practice performance. Rather, quantitative measures must be of clinical relevance and applicable to the patient populations of interest in order to create conversation and impact change. Thus, the focus of quality improvement should not be to improve numbers relating to efficiency, patient satisfaction and continuity of care, but rather to determine what drives those numbers and how changes might be made at a system or practice level that will optimise clinician buy-in.

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)0.052
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0520.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.618
GPT teacher head0.684
Teacher spread0.066 · 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 designObservational
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
Published2018
Admission routes2
Has abstractyes

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