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Record W2951152865 · doi:10.7202/1060048ar

Les enjeux éthiques de la formation clinique en ergothérapie

2019· article· fr· W2951152865 on OpenAlexafffundvenue
Marie-Josée Drolet, Anick Sauvageau, Nancy Baril, Rébecca Gaudet

Bibliographic record

VenueApproches inductives Travail intellectuel et construction des connaissances · 2019
Typearticle
Languagefr
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersUniversité du Québec à Trois-Rivières
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Les stages occupent une place centrale dans la formation des futures ergothérapeutes, mais les enjeux éthiques de la formation clinique en ergothérapie sont peu documentés. Une étude auprès de 23 ergothérapeutes ayant de l’expérience comme superviseur de stage 1 a été menée afin d’accroître les données sur cet aspect de la profession. Il ressort des entretiens qualitatifs individuels semi-dirigés réalisés avec les participantes que ceux-ci vivent tous des enjeux éthiques comme formateurs cliniques. L’analyse des verbatims fait émerger des enjeux correspondant à six thèmes, soit : 1) les conflits de loyautés multiples; 2) les étudiantes en difficulté ou en situation d’échec; 3) les tensions entre quatre postures éthiques; 4) l’enseignement de l’idéal, du possible ou de rien du tout; 5) les difficultés à soutenir les compétences éthiques et culturelles; 6) les iniquités reliées à la formation clinique. L’étude révèle notamment des enjeux inédits et préoccupants. Puisque certains d’entre eux peuvent occasionner de la souffrance chez la superviseure et la stagiaire, une réflexion critique sur ce rôle de gardienne de la profession s’impose.

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)0.008
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.013
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.404
Teacher spread0.330 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2019
Admission routes3
Has abstractyes

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