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Record W4403034213 · doi:10.7202/1113340ar

Preparing for ‘intelligent and thoughtful practice’

2024· article· en· W4403034213 on OpenAlexvenueaboutno aff
Peter L. Twohig

Bibliographic record

VenueOntario History · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyComputer scienceEngineering ethicsCognitive scienceEngineering

Abstract

fetched live from OpenAlex

The shortage of health care workers in mid-twentieth century Canada prompted a number of responses, including the introduction of new kinds of workers and the reorganization of tasks, innovative education programs to streamline the supply of workers, and the expansion of university options. Developments in Kingston, Ontario, to meet the needs of occupational therapy during the 1950s and 1960s illustrate all of these strategies. This article briefly explores a formal training program to prepare occupational therapy assistants (OTAs) before considering a unique effort to streamline the education of fully-qualified occupational therapists (OTs). These programs were important because they helped to prepare the ground for the implementation of a university program in occupational therapy at Queen’s University. In a period of rapid expansion, when health care work was very fluid, occupational therapists successfully articulated the need for university-educated OTs to meet the changing needs of patients, the profession, and the health care system.

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)0.020
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.022
Scholarly communication0.0110.004
Open science0.0020.008
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0090.003

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 head0.247
GPT teacher head0.484
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2024
Admission routes2
Has abstractyes

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