Trainee‐environment interactions that stimulate motivation: A rich pictures study
Bibliographic record
Abstract
CONTEXT: Staying motivated when working and learning in complex workplaces can be challenging. When complex environments exceed trainees' aptitude, this may reduce feelings of competence, which can hamper motivation. Motivation theories explain how intrapersonal and interpersonal aspects influence motivation. Clinical environments include additional aspects that may not fit into these theories. We used a systems approach to explore how the clinical environment influences trainees' motivation and how they are intertwined. METHODS: We employed the rich pictures drawing method as a visual tool to capture the complexities of the clinical environment. A total of 15 trainees drew a rich picture representing a motivating situation in the workplace and were interviewed afterwards. Data collection and analysis were performed iteratively, following a constructivist grounded theory approach, using open, focused and selective coding strategies as well as memo writing. Both drawings and the interviews were used to reach our results. RESULTS: Trainees drew situations pertaining to tasks they enjoyed doing and that mattered for their learning or patient care. Four dimensions of the environment were identified that supported trainees' motivation. First, social interactions, including interpersonal relationships, supported motivation through close collaboration between health care professionals and trainees. Second, organisational features, including processes and procedures, supported motivation when learning opportunities were provided or trainees were able to influence their work schedule. Third, technical possibilities, including tools and artefacts, supported motivation when tools were used to provide trainees with feedback or trainees used specific instruments in their training. Finally, physical space supported motivation when the actual setting improved the atmosphere or trainees were able to modify the environment to help them focus. CONCLUSIONS: Different clinical environment dimensions can support motivation and be modified to create optimal motivating situations. To understand motivational dynamics and support trainees to navigate through postgraduate medical education, we need to take all clinical environment dimensions into account.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.014 | 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 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".