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Structured clinical case PowerPoint™ presentations for distributed learning

2007· article· en· W2073592336 on OpenAlexaffabout
Kim Blake, Ada Poranek, Kate Macculloch

Bibliographic record

VenueMedical Education · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsContext (archaeology)CurriculumMedical educationRelevance (law)Variety (cybernetics)MedicineConsistency (knowledge bases)Computer scienceMultimediaPsychologyPedagogy

Abstract

fetched live from OpenAlex

Context and setting Clinical case presentations were introduced as a core component of the paediatrics rotation. Presentations took place in a multimedia conference room on the medical school campus. Bridgit™ conferencing software allowed students at other sites to participate in the presentations in real time. The presented cases offered an opportunity to ensure the objectives of the rotation were met, allowed students in smaller centres to be involved with larger groups and provided potentially more variation in case content. Presentations were subsequently edited by faculty staff and students and posted on the university intranet (Dalmedix) for asynchronous learning. Why the idea was necessary Students are exposed to unique patients and conditions, including disorders that are seasonal. The structured clinical cases offered an opportunity to ensure a variety of diagnoses were covered. Students in smaller centres can be actively involved with larger groups and offer variation in case content. The posted cases also represented a valuable resource for students in all years who are preparing for elective work in paediatrics, clinical rotations and examinations. What was done At the end of the paediatrics rotation, student pairs presented a patient who met Year 3 paediatrics curriculum objectives. Templates for PowerPoint™ presentations were provided in order to maintain consistency in case style. Presentations were required to be 10 minutes in duration, visual, interactive, to include question and answer sessions, a basic science slide and a multiple-choice question with relevance to the case. Paper copies of the cases were distributed to faculty for checking of content. These were then edited by elective students, who subsequently posted them on Dalmedix. Students were asked to complete questionnaires regarding their learning experience, with 9 items to be answered on a Likert scale of 1–5 (1 = strongly disagree, 5 = strongly agree) and 2 open-ended questions. Currently, more questionnaire data are being collected. A total of 65 PowerPoint™ case presentations have been posted on the Dalhousie intranet, available to medical students for learning. Evaluation of results and impact Over the past 3 years, 328 students have participated. One clerk was unable to obtain consent to make a presentation from the patient and family. Case-based PowerPoint™ presentations shared using Bridgit™ conferencing software have been an invaluable learning tool for medical students. Pilot questionnaires administered in 2004−05 and 2005−06 have indicated that 87% of clerks found creating and presenting a case presentation a useful learning experience. In addition, 85% of students thought cases available on the university's intranet would be a useful learning tool and 83% of clerks found it useful to watch other groups present (mean Likert score 4·2 ± 0·6). The clerks identified 3 main strengths in the case presentation learning process: learning about specific cases; practising presentations, and patient contact. Students found writing a multiple-choice question the most challenging aspect of the presentation (mean Likert score 3·9 ± 0·8). Due to the success of the case-based presentation learning component in paediatrics, a similar format has been launched in obstetrics and gynaecology. Discussion about involving an overseas medical school is underway and the cases are to be implemented as a distributed education component in a new medical school.

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 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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.400
Teacher spread0.373 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2007
Admission routes2
Has abstractyes

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