Barrières à l’apprentissage en ligne dans un contexte de développement des compétences chez les professionnels de santé publique au Québec
Bibliographic record
Abstract
Cet article presente les resultats d’une enquete dont l’objectif est de cerner les principales barrieres a l’apprentissage en ligne dans un contexte de developpement des competences en sante publique. Un questionnaire en ligne, envoye a 66 personnes inscrites a un microprogramme de 2e cycle en sante publique, a permis de mesurer la retention des repondants constitues de deux groupes: perseverance et abandon. Des analyses descriptives et l’utilisation des tests d’association ont ete effectuees pour comparer les groupes. La charge de travail et l’environnement social et professionnel sont, davantage pour le groupe abandon, les deux principales barrieres de la formation. D’autres facteurs de moindre importance ont aussi decourage quelques repondants a poursuivre le programme. Une amelioration des conditions d’apprentissage et des mesures incitatives du milieu professionnel sont identifiees comme leviers d’action qui permettraient aux apprenants de gerer adequatement les exigences de la formation. This article presents the results of a survey which aims to identify key barriers to learning in a context of skills development in public health. An online questionnaire, sent to 66 firmware students in public health, was used to measure the retention of the respondents consist of two groups: persistence and abandonment. Descriptive analysis and use of association tests were performed to compare groups. Workload and professional and social environment are, more for the abandonment group, the two main barriers to training. Other less important factors also discouraged some respondents to continue the program. Improved learning conditions and incentives of the workplace are identified as levers that could allow learners to adequately manage the training requirements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".