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Record W2139919326

Efficacy of Teaching Clinical Clerks and Residents How to Fill Out the Form 1 of the Mental Health Act Using an e-Learning Module

2009· article· en· W2139919326 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Electronic Journal of e-Learning · 2009
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthTest (biology)Grading (engineering)Intervention (counseling)Intraclass correlationPsychologyMedical educationMedicineClinical psychologyPsychiatryPsychometrics
DOInot available

Abstract

fetched live from OpenAlex

Background: Every physician in Ontario needs to know how to fill out a Form 1 in order to legally hold a person against their will for a psychiatric assessment. These forms are frequently inaccurately filled out, which could constitute wrongful confinement and, in extreme circumstances, could lead to fines as large as $25,000. Training people to fill out a Form 1 accurately is a large task, and e-learning (Internet-based training) provides a potentially efficient model for health human resources training on the Form 1. Objective: In this study, we looked at the efficacy of an e-learning module on the Form 1 by comparing baseline knowledge and skills with posttest performance. Methods: 7 medical students and 15 resident physicians were recruited for this study from within an academic health sciences setting in Hamilton, Ontario, Canada (McMaster University). The intervention took place over 1 hour in an educational computing lab and included a pretest (with tests of factual knowledge, clinical reasoning, and demonstration of skill filling out a Form 1), the e-learning module intervention, and a posttest. The primary outcome was the change between pre- and posttest performance. A scoring system for grading the accuracy of the Form 1 was developed and two blinded raters marked forms independently. Participants were randomly assigned to one of two sequences of assessments (A then B vs B then A), with a balanced design determining which test the participants received as either the pretest or posttest. Inter-rater reliability was determined using the Intraclass Correlation. Repeated measures analysis of variance was conducted. Results: The Intraclass Correlation (ICC) as the measure for inter-rater reliability was 0.98. For all outcome measures of knowledge, clinical reasoning, and skill at filling out the Form 1 there was a statistically significant improvement between pretest and posttest performance (knowledge, F(1,21) 54.5, p<0.001; clinical reasoning, F(1,21) 9.39, p=0.006; Form 1 skill, F(1,21) 15.7, p=0.001). Further analysis showed no significant differences or interactions with other variables such as between raters, the order of assessment, or trainee type. Conclusions: Under laboratory conditions, this e-learning module demonstrated substantial efficacy for training medical students and residents on the theory and practice of filling out the Form 1 of the Mental Health Act. E-learning may prove to be an efficient and cost-effective medium for training physicians on this important medico-legal aspect of care. Further research is required to look at the longer-term impact of training and broader implementation strategies across the province for medical trainees and practicing physicians.

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.

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)0.008
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.387
Teacher spread0.353 · 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