Journey of candidates who were unmatched in the Canadian Residency Matching Service (CaRMS): A phenomenological study
Bibliographic record
Abstract
BACKGROUND: Each year, a number of medical students are unmatched in the Canadian Residency Matching Service (CaRMs) match. Blog posts from previous unmatched students suggest that being unmatched is associated with significant stress. However, no studies have explored the collective experiences of candidates who are unmatched. This study seeks to explore the experiences of Canadian students who were unmatched in the first iteration of their CaRMS applications. METHODS: This was an interview-based qualitative study using a phenomenology approach to explore the perspectives of 15 Canadian participants from seven universities who had previously experienced being unmatched between 2011 and 2017 in CaRMS. Telephone interviews were conducted using a semi-structured guide focusing on the experiences in the following domains: the overall unmatched experience; perceived reasons leading to their unmatched status; resources employed; barriers experienced; recommendations; and, their eventual career outcomes. Field notes were analyzed independently by all authors using thematic analysis and authors independently identified major themes. To reconcile divergent impressions and better situate qualitative impressions of our participants, we used publicly available quantitative data from CaRMS to calculate relevant odds ratios. RESULTS: Our participants universally reported negative emotions, concerns regarding privacy and confidentiality breaches, and stigma faced. Systemic challenges faced by our participants included: lack of information, pressures perceived from undergraduate medical education to apply in the second iteration to specialties that they did not want, and logistical issues such as financial challenges, licensing and scheduling issues. The utility of peer support differed for individual participants, but all those who had support from other unmatched candidates felt that to be useful. CONCLUSIONS: Our participants reported significant challenges faced after being unmatched. Based on these experiences, we identified key areas of support needed for candidates through their unmatched journey.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".