MétaCan
Menu
Back to cohort
Record W7118006362 · doi:10.15453/2168-6408.2377

The Need for ‘Soft Skills’ in Occupational Therapy Curricula: A Descriptive Analysis of French Students’ Internship Comments

2025· article· en· W7118006362 on OpenAlexaff
Cynthia Engels, Nadia Oubaya, Aliki Thomas

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsInternshipSoft skillsOccupational therapyCurriculumAccountabilityDescriptive statistics

Abstract

fetched live from OpenAlex

A competent occupational therapist needs soft skills. Although the French national occupational therapy (OT) curriculum lists 10 competences to be acquired, they do not include soft skills. The objectives of this study were to (a) Identify the proportion of soft skills in OT students’ internship comments written by internship tutors and (b) characterize the mentioned soft skills to help determine whether and which soft skills should be included in the new OT curriculum nationwide in France. This was a qualitative descriptive analysis of tutors’ comments on internship evaluations based on the framework method. Of the 1060 internship reports, 1035 (98%) for 217 students mentioned at least one soft skill. Taking initiatives was the most frequently mentioned (65%), followed by accountability (62%) and adaptability (46%). Taking initiatives, communication, and organization were the most negatively mentioned (respectively in 36%, 18% and 17% of the comments). The 36% of negative comments about taking initiatives concerned 86% of the students. Although soft skills are not taught in the French OT curriculum, most internship reports mentioned them. If students were taught these soft skills during their OT education, they may be better able to improve them throughout their training.

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)0.009
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.000
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 head0.315
GPT teacher head0.580
Teacher spread0.265 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Open Journal of Occupational TherapySame topicOccupational Therapy Practice and ResearchFrench-language works237,207