Goal co‐construction and dialogue in an internal medicine longitudinal coaching programme
Bibliographic record
Abstract
BACKGROUND: Longitudinal coaching in residency programmes is becoming commonplace and requires iterative and collaborative discussions between coach and resident, with the shared development of goals. However, little is known about how goal development unfolds within coaching conversations over time and the effects these conversations have. We therefore built on current coaching theory by analysing goal development dialogues within resident and faculty coaching relationships. METHODS: This was a qualitative study using interpretive description methodology. Eight internal medicine coach-resident dyads consented to audiotaping coaching meetings over a 1-year period. Transcripts from meetings and individual exit interviews were analysed thematically using goal co-construction as a sensitising concept. RESULTS: Two themes were developed: (i) The content of goals discussed in coaching meetings focused on how to be a resident, with little discussion around challenges in direct patient care, and (ii) co-construction mainly occurred in how to meet goals, rather than in prioritising goals or co-constructing new goals. CONCLUSIONS: In analysing goal development in the coach-resident relationships, conversations focused mainly around how to manage as a resident rather than how to improve direct patient care. This may be because academic coaching provides space separate from clinical work to focus on the stage-specific professional identity development of a resident. Going forward, focus should be on how to optimise longitudinal coaching conversations to ensure co-regulation and reflection on both clinical competencies and professional identity formation.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".