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Record W2918028583 · doi:10.1177/2374373519830711

Assessing Performance in Health Care Using International Surveys: Are Patient and Clinician Perspectives Complementary or Substitutive

2019· article· en· W2918028583 on OpenAlexaff
Jean‐Frédéric Lévesque, Lisa Corscadden, Anushree Davé, Kim Sutherland

Bibliographic record

VenueJournal of Patient Experience · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsMcGill University
Fundersnot available
KeywordsConcordanceCommonwealthMedicineFamily medicineComplementarity (molecular biology)Health careSurvey data collectionPsychologyPolitical scienceStatistics

Abstract

fetched live from OpenAlex

Background: Over the last decade, international surveys of patients and clinicians have been used to compare health care across countries. Findings from these surveys have been extensively used to create aggregate scores and rankings. Objective: To assess the concordance of survey responses provided by patients and clinicians. Methods: Analysis of 16 pairs of questions that focused on coordination, organizational factors, and patient-centered competencies from the Commonwealth Fund International Health Policy Survey of older adults (2014) and of primary care physicians (2015). Concordance was assessed by comparing absolute rates and relative rankings. Results: In absolute terms, patients and clinicians gave differing responses for questions about coordination of care (patients were more positive) and provision of after-hours care (patients were less positive). In relative terms, country rankings were positively correlated for 5 of 16 question pairs (Spearman ρ > .6 and P < .05). Conclusion: Patterns of concordance between patient and clinician perspectives provides information to guide the use of survey data in performance assessment. However, this study highlights the need to assess the complementarity and substitutive nature of patients’ and clinicians’ perspectives before combining them to create aggregate assessments of performance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.169
GPT teacher head0.509
Teacher spread0.341 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2019
Admission routes1
Has abstractyes

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