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Record W1567608652

Improving patient satisfaction with time spent in an orthopedic outpatient clinic.

2000· article· en· W1567608652 on OpenAlexaff
J Levesque, Earl R. Bogoch, B Cooney, B. C. Johnston, J G Wright

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineOutpatient clinicPatient satisfactionOrthopedic surgeryProspective cohort studyPhysical therapyFamily medicineInternal medicineSurgery
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine if patient satisfaction can be improved by changing patients' expectations of the clinic visit and by decreasing the total time spent in the clinic. DESIGN: A prospective comparative analysis carried out in 4 phases. SETTING: An university-affiliated orthopedic outpatient clinic. PATIENTS: All patients seen in the orthopedic outpatient clinic were eligible. Phase 1 determined the total clinic time required by patient type; phase 2 assessed baseline satisfaction; phase 3 altered patients' expectations; and phase 4 altered patients' expectations and scheduled visits by patient type. INTERVENTION: Patient questionnaires. MAIN OUTCOME MEASURE: Patient satisfaction with time spent in the clinic. RESULTS: Of 708 distributed questionnaires, 622 (88%) were completed (547 totally complete, 75 partially complete). Total time spent in the clinic decreased across phases 2, 3 and 4 (mean 99.2, 94.7 and 85.2 minutes, respectively, but was significantly different only between phases 3 and 4; p = 0.05, Duncan's multiple range test). The percentage of patients who rated their waiting time as "excellent" increased across phases 2, 3 and 4 (14.6%, 18.8% and 31.1%, respectively; p = 0.0004, chi 2 test). CONCLUSION: Patient satisfaction can be improved by altering patient expectations and by decreasing the total time spent in clinic.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.999

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)0.0060.001

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 head0.064
GPT teacher head0.357
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

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

Citations44
Published2000
Admission routes1
Has abstractyes

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