Continuity interrupted: exploring discontinuity of education and mitigation strategies in family medicine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Continuity of education (CoE) is a growing area of interest in health professions education, both for its impacts on learning (continuity of curriculum and continuity of supervision; CoS) and for its influence on patient care (continuity of patient care; CoPC). The COVID-19 pandemic offered an opportunity to examine discontinuity of education and the potential impacts of interruption to CoE, a knowledge gap in medical education research. METHODS: We conducted 14 semi-structured qualitative interviews involving participants from a Canadian family medicine programme. We recorded and transcribed interviews conducted on Zoom that were then analysed iteratively using reflexive thematic analysis to identify major themes. RESULTS: We identified three themes. Theme 1: Changed relationships: an alteration due to mitigation strategies. Theme 2: Preparedness for practice: a decrease despite mitigation strategies. Theme 3: Adaptivity in the face of change: a consequence of mitigation strategies. CONCLUSION: This study suggests that there are three main implications resulting from the impacts of disruption to CoE. Faculty development and curricular design are needed to support interrupted relationships, including finding ways to help faculty and residents nurture changed relationships. Physicians in their first 5 years of practice who have experienced disruption in their training may benefit from additional support to address the negative impact on their sense of preparedness for practice. Finally, the positives learned from this study can be used to face future disruptions to CoE.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it