Goal-Setting on a Geriatric Medicine Rotation: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Formal goal-setting has been shown to enhance performance and improve educational experiences. We initiated a standardized goal-setting intervention for all residents rotating through a Geriatric Medicine rotation. OBJECTIVES: This study aims to describe the feasibility of a goal-setting intervention on a geriatric medicine rotation, the resources required, and the barriers to implementation. As well, this study aims to describe the learning goals residents created regarding content and quality. METHODS: A pilot goal-setting intervention was initiated. A goal-setting form was provided at the beginning of their rotation and reviewed at the end of the rotation. Residents were invited to complete an anonymous online survey to gather feedback on the initiative. Goals were analysed for content and quality. Feedback from the survey results was incorporated into the goal-setting process. RESULTS: Between March and December 2018, 26 of 44 residents completed the goal-setting initiative. Explanations for the poor adherence included limited protected time for faculty and residents to engage in coaching, its voluntary nature, and trainee absence during orientation. Reasons for difficulty in achieving goals included lack of faculty and trainee time and difficulty assisting residents in achieving goals when no clinical opportunities arose. Although only 59% of residents completed the intervention, if goal-setting took place, most of the goals were specific (71 of 77; 92%) and 35 of 77 (45.5%) goals were not related to medical knowledge. CONCLUSIONS: This pilot study outlines the successes and barriers of a brief goal-setting intervention during a Geriatric Medicine rotation. Adherence was limited; however, of those who did complete the intervention, the creation of specific goals with a short, structured goal-setting form was possible. To enhance the intervention, goal-setting form completion should be enforced and efforts should be made to engage in mid-rotation check-ins and coaching.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".