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Record W4396755891 · doi:10.7202/1110997ar

Development and Validation of a Measurement Scale for the Professionalization of University Students in Health Sciences

2022· article· en· W4396755891 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMesure et évaluation en éducation · 2022
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsUniversité LavalUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsProfessionalizationScale (ratio)Health sciencePsychologyMedical educationMathematics educationSociologyMedicineSocial scienceGeographyCartography

Abstract

fetched live from OpenAlex

This article presents the results of a study aimed at constructing and validating a scale for measuring the professionalization of health sciences students. Evidence of the content, response process, and internal structure of the scale was provided throughout the study, including data collection from 561 undergraduate and graduate students from four Quebec universities. The results of an exploratory factor analysis indicated a very good internal consistency and support for a simple four-factor structure. Thus, a fourth factor (valuing the profession) was added to the three factors (professional skills, identity, and culture) set out in an initial conceptual framework. The results of a confirmatory factor analysis revealed that these four first-order factors were related to a single second-order factor of professionalization. This scale provides a robust instrument that can be used for studying the professionalization of students at different phases of their educational journey.

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)0.024
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.677
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.000
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 head0.172
GPT teacher head0.450
Teacher spread0.278 · 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