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Record W777801583 · doi:10.1007/s40596-015-0377-y

Evaluation of a National Online Educational Program in Geriatric Psychiatry

2015· article· en· W777801583 on OpenAlexaffabout
Marcus Law, Mark Rapoport, Dallas Seitz, Marla Davidson, Robert Madan, Andrew Wiens

Bibliographic record

VenueAcademic Psychiatry · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of OttawaUniversity of SaskatchewanQueen's UniversityHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)Wilcoxon signed-rank testMedical educationDescriptive statisticsPsychologyGeriatricsComputer-assisted web interviewingAsynchronous communicationOnline learningMedicineCurriculumPsychiatryComputer scienceMultimediaPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: This study provides evaluation results of an online study group (OSG) for geriatric psychiatry continuing professional development. METHODS: The OSG is an interactive, expert-facilitated, asynchronous educational experience for psychiatrists and residents in Canada. A retrospective web survey assessed self-efficacy, knowledge in geriatric psychiatry, comfort with online learning, and perceived effectiveness of the instructional methods. Wilcoxon signed-rank tests and descriptive statistics were calculated. RESULTS: Twenty-nine (of 50) participants (58 %) completed the questionnaire. Although only 48 % of respondents reported improved perceived knowledge, 79 % reported improved efficacy beliefs, and 76 % reported improved comfort with online learning. Most (79 %) would consider taking OSG again, and 93 % would recommend it to others. CONCLUSIONS: The OSG was well-received, with greater benefits for self-efficacy with the material and comfort with online learning than for perceived knowledge itself. Further research is needed to ascertain actual knowledge change in the context of online learning in medical education.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.071
GPT teacher head0.423
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

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 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

Citations12
Published2015
Admission routes2
Has abstractyes

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