“The Feeling of Failure Is Immense”: A Qualitative Analysis of the Experiences of Unmatched Residency Applicants Using Reddit
Bibliographic record
Abstract
PURPOSE: The increasing number of applicants who do not match to a residency program has been identified as an issue in Canada and the United States, yet the experiences of those individuals going unmatched have remained largely unexplored. By better understanding such experiences, medical schools will be better equipped to support unmatched graduates. The authors sought to understand the shared experiences of unmatched residency applicants, including those who did not match to American residency programs. METHOD: The authors turned to the social media platform Reddit as their source of data to further understand the experiences of unmatched residency applicants, using a thematic analysis of comments posted on the subreddit r/medicalschool. The subreddit was searched for comments containing the term unmatched on August 16, 2021. A total of 206 comments containing content about first-person experiences of going unmatched were included in the thematic analysis. RESULTS: The 206 comments were authored by 124 unique usernames between the years of 2012 and 2021. Two primary themes emerged in the analysis: perceptions of insufficient return for investments made in medical education and concern about the possibility of going unmatched again in the future. Subthemes identified for the first theme were regret for pursuing medical school, perceptions of failure, and considering an alternative career. Subthemes identified for the second theme were perceived lack of transparency and fairness in the match process and desire to improve future applications, which sometimes involved considering applying to other specialties. CONCLUSIONS: The results of this study suggest unmatched applicants are prone to a range of negative cognitive and emotional reactions. In light of these findings, medical schools should ensure effective supports are being offered to unmatched applicants in a timely and nonjudgmental manner. Future work should focus on investigating the outcomes of interventions for supporting unmatched applicants.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".