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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".