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Record W3108928795 · doi:10.1177/0008417420968683

Investigating Open-access Booking in New Brunswick Occupational Therapy

2020· article· en· W3108928795 on OpenAlexvenueaboutno aff
David Speed, Shelley Fyffe

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsInterrupted time seriesMedicineService (business)NursingBusinessPsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND.: Open-access booking (OAB) describes administrative changes to improve system efficiency. However, OAB studies have focused on GP practices and have not applied OAB to other health care services. PURPOSE.: The purpose of the study was to investigate the associations between OAB and administrative outcomes in the Saint John region. METHOD.: Evaluators compared three years of pre-OAB data against two years of post-OAB data using an interrupted-time series design (February 2014-January 2019). FINDINGS.: OAB was associated with a 12% jump in the likelihood of being discharged within three months even though clients received an equivalent level of service. OAB was not associated with more missed appointments (∼8% vs. ∼7%). While OAB was not associated with reduced wait times, the post-OAB period handled a larger number of client referrals, which may explain the null finding. IMPLICATIONS.: OAB shows potential for improving administrative outcomes, but further research is needed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.346
GPT teacher head0.412
Teacher spread0.067 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations2
Published2020
Admission routes2
Has abstractyes

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