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Record W2410933262 · doi:10.1093/ajhp/57.17.1585

Opinions on provider profiling: Telephone survey of stakeholders

2000· article· en· W2410933262 on OpenAlexaff
Neil J. MacKinnon, Earlene E. Lipowski

Bibliographic record

VenueAmerican Journal of Health-System Pharmacy · 2000
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsDalhousie University
Fundersnot available
KeywordsProfiling (computer programming)Snowball samplingHealth carePsychologyMedicineMedical educationComputer science

Abstract

fetched live from OpenAlex

The views of producers, purchasers, and users of provider profiling concerning this practice were studied. A snowball sample of individuals representing seven groups with a stake in retrospective provider profiling were interviewed by telephone over a 12-week period in 1997. Participants were asked what they believed were the most important uses for profiles, who should receive copies of profiles, and what the limitations of profiles are. A semi-structured format was used to ensure that each interview was comparable and complete. The responses were aggregated, and qualitative research approaches were used to analyze them. A total of 40 people were interviewed. A majority of the respondents cited physician education, changing physician behavior, and monitoring and improving the quality of care as valid uses of provider profiles. A majority believed that the recipients of profile data should include the individual providers being profiled, medical administrative staff, people directly involved in the profiling program, pharmacists, and health plan administrators. The respondents acknowledged many limitations of profiles, with the top concern being inherent problems in the use of billing and administrative databases for profiling. Interviews with stakeholders in provider profiling yielded insights into the strengths and weaknesses of profiling, as well as echoing findings reported elsewhere. Health system administrators and health care professionals need to be aware of these issues as they use and modify profiling.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.254
GPT teacher head0.480
Teacher spread0.225 · 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

Citations6
Published2000
Admission routes1
Has abstractyes

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