Psychological need fulfillment in virtual teaching: insights of residents and faculty
Bibliographic record
Abstract
Objective: To explore benefits and challenges experienced by residents and faculty when teaching in virtual settings. Methods: This was a qualitative descriptive study employing one-on-one semi-structured interviews with 10 residents and 12 faculty in the Department of Family Medicine at the University of Alberta, Canada, from May 2021 to May 2022. Participants were recruited via social media, resident and department events and email lists. Interview transcripts were analyzed descriptively and thematically employing the Self-Determination Theory (SDT) framework to map the identified benefits and challenges as facilitators and barriers to fulfilling teacher's basic psychological needs for autonomy, competence, and relatedness in virtual settings. Results: Resident and faculty participants used virtual technology not only to deliver education, but also leveraged various platform features to support their needs in virtual settings. The emerging themes within benefits and challenges of virtual teaching were amenable to mapping onto three basic psychological needs of the SDT framework - autonomy (e.g., increased accessibility; lack of control over teaching environment), competence (e.g., increased self-confidence; technological limitations hindering skill development), and relatedness (e.g., timely exchange of information; difficulty with professional identity formation). Conclusions: Despite the inherent challenges, teaching in virtual settings can support teachers' psychological needs. Recommendations for the future delivery and facilitation of virtual learning include: giving high priority to engagement and active participation; nurturing autonomy and greater individual responsibility for learning; and creating an environment of emotional support. The SDT-informed strategies shown to be effective in in-person teaching need to be examined for their applicability in virtual settings.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".