Impact of personal goals on the internal medicine R4 subspecialty match: a Q methodology study
Bibliographic record
Abstract
BACKGROUND: There has been a decline in interest in general internal medicine that has resulted in a discrepancy between internal medicine residents' choice in the R4 subspecialty match and societal need. Few studies have focused on the relative importance of personal goals and their impact on residents' choice. The purpose of this study was to assess if internal medicine residents can be grouped based on their personal goals and how each group prioritizes these goals compared to each other. A secondary objective was to explore whether we could predict a resident's desired subspecialty choice based on their constellation of personal goals. METHODS: We used Q methodology to examine how postgraduate year 1-3 internal medicine residents could be grouped based on their rankings of 36 statements (derived from our previous qualitative study). Using each groups' defining and distinguishing statements, we predicted their subspecialties of interest. We also collected the residents' first choice in the subspecialty match and used a kappa test to compare our predicted subspecialty group to the residents' self-reported first choice. RESULTS: Fifty-nine internal medicine residents at the University of Alberta participated between 2009 and 2010 with 46 Q sorts suitable for analysis. The residents loaded onto four factors (groups) based on how they ranked statements. Our prediction of each groups' desired subspecialties with their defining and/or distinguishing statements are as follows: group 1 - general internal medicine (variety in practice); group 2 - gastroenterology, nephrology, and respirology (higher income); group 3 - cardiology and critical care (procedural, willing to entertain longer training); group 4 - rest of subspecialties (non-procedural, focused practice, and valuing more time for personal life). There was moderate agreement (kappa = 0.57) between our predicted desired subspecialty group and residents' self-reported first choice (p < 0.001). CONCLUSION: This study suggests that most residents fall into four groups based on a constellation of personal goals when choosing an internal medicine subspecialty. The key goals that define and/or distinguish between these groups are breadth of practice, lifestyle, desire to do procedures, length of training, and future income potential. Using these groups, we were able to predict residents' first subspecialty group with moderate success.
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 | 0.072 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".