Experiences of remediation in family medicine postgraduate education: An interpretative phenomenological analysis
Bibliographic record
Abstract
PURPOSE: Remediation programs in postgraduate medical education aim to help struggling residents successfully complete their training and graduate. Research has mostly focused on educators' and program directors' perspectives on the development and quality of remediation programs. However, little is known about the experience of being remediated. This investigation aimed to understand how practicing family physicians who underwent remediation during their residency experienced their remediation program. MATERIALS AND METHODS: In this Interpretative Phenomenological Analysis (IPA) study, we purposively sampled six family medicine practitioners who underwent successful remediation as residents in a Canadian medical school. We individually interviewed them in-depth, one to six years after graduation. For data analysis, we combined IPA thematic and narrative techniques. RESULTS: While each participant had a unique experience of remediation, we identified a common longitudinal process of four consecutive periods: pre-remediation, remediation, and early and late post-remediation. All participants experienced the remediation announcement as shocking, surprising, and disruptive, as most were unaware that they were not reaching the expected competency standards. This triggered the hot-cognition learning process that characterized the initial phases of their remediation experience. During remediation, they went through a progressive reorganization of a self-coherent professional identity formation process, mediated by the interactions with their educators. In the early post-remediation phase, most participants accepted their teachers' and institution's educational evaluations and expectations. Some, however, started developing a dissonant professional identity vis-à-vis their community of practice. In the late post-remediation phase, most participants viewed their remediation experiences as beneficial to their career development, but others felt lasting harm, evidenced by less career satisfaction and practice advancement. DISCUSSION: The experience of being remediated in postgraduate medical education is an emotionally charged one. It is modulated by a variety of personal factors and surrounding features. The quality of interactions between residents who need remediation and their remediators appears to be a critical element in the success of a remediation program. Remediation, even when successful, is a shocking and emotionally challenging educational experience for those who undergo it, with important and lasting implications.
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How this classification was reachedexpand
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".