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Record W2014018296 · doi:10.1080/10401330903228554

The Impact of a Personal Digital Assistant (PDA) Case Log in a Medical Student Clerkship

2009· article· en· W2014018296 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTeaching and Learning in Medicine · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Medical education literature emphasizes that reflection and self-audit are pivotal steps in learning and that personal digital assistants (PDAs) have potential as decision support tools. DESCRIPTION: The purpose was to examine the efficacy of PDA-based resources and patient-encounter logging systems among 3rd-year medical clerks during pediatrics rotations. EVALUATION: Students in rotations were assigned to control (using paper-based logs and references) or intervention groups (using PDA-based logs and resources). Students completed pre- and postrotation Paediatrics Competency Surveys, participated in focus groups, and were compared on year-end examination grades. Use of PDA logs far outweighed that of paper logs (1,020 PDA logs and 87 paper logs). PDA logs were ranked significantly higher in enhancing learning and reflection than paper logs (t = 2.52, p < .01). PDA logs also facilitated specific learning experiences. CONCLUSION: PDA-based patient-encounter logs appear to be effective case documentation and reflection tools. The difference in number of logs between control and intervention groups demonstrates the utility of the PDA for "point-of-care" patient logging.

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)0.010
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.475
Teacher spread0.441 · 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