We should <i>nudge</i> clinicians and trainees to participate in health professions education programmes
Bibliographic record
Abstract
Health Professional Education (HPE) programmes, such as mentorship, are widely regarded as being advantageous to the personal and professional development of clinicians and trainees. Involvement in a mentoring relationship is associated with positive outcomes for both mentees and mentors, including improved career preparation, increased career success, higher job satisfaction and reduced risk of burnout. Despite these data, a minority of trainees report having a mentor. In this Cross-Cutting Edge article, the authors focus on an impediment to participation in HPE programmes that they feel are both highly prevalent and modifiable: habit. Taking the example of mentorship, they use dual processing as their theoretical framework and describe how we use both System 1 and System 2 processing to make decisions that, in turn, promote habitual and goal-directed actions, respectively. The authors discuss the relationship between habitual and goal-directed actions and suggest that habits can both facilitate and hinder our goals. Drawing on the clinical literature on adherence to clinical practice guidelines, they describe how habits and contextual factors can interfere with clinical goals and how manipulating the clinical environment can move behaviour in the desired direction. They then branch into behavioural economics to describe the features of a nudge (and a sludge) and review the literature on the effectiveness of this type of intervention - including potential ethical concerns around the use of nudges as behavioural interventions. Using the MINDSPACE mnemonic/framework they suggest different types of transparent and non-transparent nudges that could be used to increase participation in mentorship. Recognizing that mentorship is complex and the impact of a single nudge on behaviour may be ineffective or wane over time, the authors propose a process of ongoing programme evaluation and quality improvement that could help create and maintain a culture of mentorship and that can also be applied to other HPE programmes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".