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Record W2468160579 · doi:10.1080/13561820.2016.1181614

An evaluation of wait-times at an interprofessional student-run free clinic

2016· article· en· W2468160579 on OpenAlexaff
Tina Hu, Fok‐Han Leung

Bibliographic record

VenueJournal of Interprofessional Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsFree clinicMedicineInterprofessional educationReferralMedical prescriptionWaiting listFamily medicineMedical emergencyNursingHealth careSurgery

Abstract

fetched live from OpenAlex

Student-run free clinics (SRFCs) are becoming increasingly popular programmes in schools for promoting interprofessional education and service learning. Despite their prevalence, little research has been done surrounding SRFCs and wait-times. SRFCs may experience long wait-times because patients are often seen on a drop-in basis and time has to be allotted for interprofessional case discussion and teaching. The purpose of this study was to evaluate the wait-times for patients being seen at an interprofessional SRFC and determine potential improvements. Wait-times, total treatment room times, total time per patient, and whether a referral and/or prescription was given were tracked from May 2014 to July 2015 at an SRFC. A total of 268 patients were seen in 52 clinics. On average, five patients were seen per clinic. Patients waited for an average time of 21 minutes before they were seen by the team. Average treatment time per patient was 69 minutes. Patients were generally at the clinic for a total of 91 minutes before being discharged. Several improvements for managing client flow at interprofessional SRFCs are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.000

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 head0.056
GPT teacher head0.519
Teacher spread0.463 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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