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Record W2057572433 · doi:10.3109/13561820.2014.977380

Student perspectives of an online module for teaching physical assessment skills for dentistry, dental hygiene, and pharmacy students

2014· article· en· W2057572433 on OpenAlexaff
Christine Leong, Christopher Louizos, Chelsea Currie, Lorraine Glassford, Neal M. Davies, Douglas J. Brothwell, Robert Renaud

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPharmacyLikert scaleCurriculumMedical educationDental hygieneMedicineHygieneDentistryPsychologyFamily medicinePedagogy

Abstract

fetched live from OpenAlex

The integration of web-based learning into the curriculum of healthcare education has significantly increased over the past decade. This article aims to describe the student perspectives of an online module to teach physical assessment skills for pharmacy, dentistry, and dental hygiene students. A total of 103 students completed the online module: 48 third-year pharmacy students, 29 first-year dentistry students, and 26 first-year dental hygiene students. Students were asked to rate a list of 10 statements on a 5-point Likert scale on the relevance, impact, and overall satisfaction of the online module. Eighty-four of the 103 students (81.6% response rate) completed the questionnaire. While most students responded positively to the online content, pharmacy students responded more favorably compared with students from Dentistry and Dental Hygiene. These findings provide useful information to identify areas in which the web-based module can be improved for teaching skills in physical assessment across multiple healthcare programs.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.485
Teacher spread0.468 · 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 designQualitative
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

Citations18
Published2014
Admission routes1
Has abstractyes

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