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Exploring Professional Development Needs of Educators in the Health Sciences Professions

2015· article· en· W1895479465 on OpenAlexaffabout
Dieter J. Schönwetter, Joanne Hamilton, Jo‐Ann V. Sawatzky

Bibliographic record

VenueJournal of Dental Education · 2015
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsManitoba HealthUniversity of Manitoba
Fundersnot available
KeywordsUnit (ring theory)Medical educationFaculty developmentProfessional developmentNeeds assessmentHigher educationBiomedical sciencesPsychologyHealth professionsMedicineHealth careSociologyNursingPolitical scienceMathematics educationSocial science

Abstract

fetched live from OpenAlex

An increasing number of institutions of higher education are clustering their health sciences schools into a common unit. Therefore, it is imperative that the individual faculty development units assume new mandates to meet faculty development needs for stakeholders across these disciplines. Critical to providing current and relevant professional development activities is an awareness of the needs of academicians, including common as well as discipline-specific needs. Hence, the aim of this study was to explore the extent to which factors such as discipline, rank, gender, education, and years as an academician impact on perceived needs for faculty development. In February 2012, a cross-sectional survey of the perceived faculty development needs of academicians in the health sciences unit of a Canadian university was conducted using an online assessment tool. A total of 133 out of 1,409 potential participants completed the survey, for a response rate of 9.4%. The findings revealed more similarities than differences in terms of perceived faculty development needs. In addition, differences were found across all health professions schools and in factors such as discipline, academic rank, education, gender, and years as an academician. These findings suggest that faculty development and educational specialists should understand the shared as well as the unique needs of the individual health sciences schools in planning their professional development services.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.443
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.161
GPT teacher head0.454
Teacher spread0.292 · 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 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

Citations36
Published2015
Admission routes2
Has abstractyes

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