How to Succeed in Research During Medical Training: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: The objective of this study was to examine the characteristics of the medical trainee (resident), the supervisor and the project that contribute to successful completion of resident-led research and publication in a peer-reviewed scientific journal. METHODS: Qualitative, interview-based study of Internal Medicine trainees and their supervisors. All interviewed trainees published at least one first-author research paper based on a project they completed during residency. Thematic analysis was used to explore key themes from interview transcripts. An iterative, team-based approach was used to develop a coding framework, which was then applied to the data and summarized. Six investigators independently reviewed and coded transcripts, discussed the data collectively and developed key themes by consensus. RESULTS: Thirty participants (15 residents and 15 supervisors) were interviewed. Three major themes for successful resident research projects emerged: 1) the resident is the project champion; 2) supervisors ensure feasibility and timeliness of the project; and, 3) limited time is a challenge that can be overcome. Residents were motivated by fellowship aspirations, prioritized the project and were genuinely interested in the content area. Supervisors were responsible for setting deadlines, limiting the scope of the project and ensuring feasibility of the study design. Existing research funds and infrastructure from other projects were frequently used by supervisors to support research done by trainees. CONCLUSIONS: Successful resident-led research projects require leadership and motivation by the resident and engagement, reality-checking and deadline-setting by the supervisor. Responsibilities and expectations in the resident-supervisor relationship should be set early and adequate program resources and funding are required.
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 | 0.057 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".