Development of complex pedagogical competencies and reflexivity in clinical teachers via distance learning: a mixed methods study
Bibliographic record
Abstract
Clinical reasoning is the cornerstone to healthcare practice and teaching it appropriately is of utmost importance. Yet there is little formal training for clinical supervisors in supervising this reasoning process. Distance education provides interesting opportunities for continuous professional development of healthcare professionals. This mixed methods study aimed at gaining in-depth understanding about whether and how clinical teachers can develop complex pedagogical competencies through participation in a Massive Open Online Course on the supervision of clinical reasoning (MOOC SCR). Participants self-assed their clinical supervision skills before and after partaking in the MOOC SCR through the Maastricht Clinical Teachers Questionnaire. Item scores and the distribution of response proportions before and after participation were compared using paired t-tests and McNemar's tests respectively. In parallel, the evolution of a subset of MOOC participants' pedagogical practice and posture was explored via semi-structured interviews throughout and beyond their MOOC participation using simulated and personal situational recalls. The verbatim were analysed with standard thematic analysis. Quantitative and qualitative findings converged and their integration demonstrated that partaking in the MOOC SCR promoted the development of complex pedagogical competencies and reflexivity with the participants. This was quantitatively evidenced by significantly higher self-assessed supervision skills and corresponding attitudes after completing the MOOC. The qualitative data provided rich descriptions of how this progression in pedagogical practice and posture occurred in the field and how it was shaped by participants' interaction with the MOOC's content and their motivations to progress. Our findings provide evidence for the development of pedagogical skills and corresponding attitudes for the supervision of clinical reasoning through participation in the MOOC SCR and contribute to the literature body on the opportunities that distance learning provides for the development of pedagogical competencies. The extent to which the pedagogical underpinnings of the MOOC contributed to these developments remains to be determined.
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 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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".