Ready, Set, Goal: A Mixed Methods Study of a Goal-Setting Intervention on 2 Competency-Based Geriatric Medicine Rotations
Bibliographic record
Abstract
ABSTRACT Background More research is required to understand the effects of implementing structured goal-setting on trainee engagement in competency-based clinical learning environments. Objective To explore how residents experienced a rotation-specific goal-setting intervention on geriatric medicine rotations at 2 hospitals. Methods All rotating residents were expected to complete the intervention, consisting of a SMART-based (Specific, Measurable, Achievable, Relevant, and Time-Bound) goal-setting form and feedback sessions with teaching faculty. From November 2019 to June 2021, we recruited a convenience sample of rotating residents. Study participants completed pre- and postrotation 35-item Dutch Residency Educational Climate Test (D-RECT) questionnaires to compare scores from their rotation before the geriatric rotation and a postrotation semistructured interview, which we transcribed and analyzed using principles of constant comparison and reflexive thematic analysis. Results We interviewed 12 of 58 (20.7%) residents participating in the goal-setting intervention, 11 of whom completed both D-RECT questionnaires. Participants’ D-RECT scores favored the geriatric medicine rotation versus the immediately preceding clinical rotation (M=4.29±0.37; M=3.84±0.44, P =.002). Analyses of interview transcripts yielded 3 themes on how participants perceived the intervention influenced their learning experience: (1) structured forms and processes mediate, inform, and constrain goal selection; (2) interactions with faculty, patients, and system factors influenced goal enactment; and (3) unstructured assessments led to uncertainty around goal achievement. Challenges included time restrictions and unpredictable clinical opportunities. Conclusions Goal-setting appeared to help many residents direct their learning efforts and engage in collaborative processes with teaching faculty. We identified challenges limiting residents’ engagement with the goal-setting intervention, which may inform the practical implementation of goal-setting in other competency-based curricula.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".