Assessing the Efficacy of an Individualized Psychological Flexibility Skills Training Intervention App for Medical Student Burnout and Well-being: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Medical student burnout is a prevalent problem with adverse long-term outcomes. Incorporating psychological resource-building interventions into comprehensive burnout prevention approaches during medical training is an identified priority among educators. These interventions could reduce burnout risk by buffering students against nonmodifiable career stressors. However, there is a need for rigorous investigation into optimal intervention targets and methods. Psychological flexibility (PF) is an adaptive behavioral skill set that has demonstrated relationships with medical student burnout and well-being. More broadly, there is evidence that PF mediates burnout and well-being outcomes and may be a protective factor. Efficacy studies assessing the benefits of interventions targeting PF among medical students are needed. Research also supports the need to establish optimal methods for increasing intervention efficacy in the context of individual differences in burnout and PF by using individualized approaches. OBJECTIVE: This study aims to assess whether an app-delivered PF intervention (Acceptance and Commitment Training) reduces burnout and improves well-being among medical students. We will examine whether changes in burnout and well-being are mediated by changes in PF. The potential benefits of an individualized version of the app versus those of a nonindividualized version will also be evaluated. METHODS: In this 3-arm, parallel, randomized controlled study, a sample of medical students will be randomly allocated to 1 of 3 intervention arms (individualized, nonindividualized, and waiting list) by using a 1:1:1 allocation ratio. Participants in the individualized and nonindividualized intervention arms will have 5 weeks to access the app, which includes a PF concepts training session (stage 1) and access to short PF skill activities on demand (stage 2). Stage 2 will be either individualized to meet participants' identified PF training needs at each log-in or nonindividualized. RESULTS: Burnout, well-being, and PF will be assessed at baseline and after the intervention. Quantitative analyses will include descriptive and inferential statistics. We hypothesize that the Acceptance and Commitment Training intervention app will be effective in improving burnout and well-being and that changes in these outcomes will be mediated by changes in PF. We further hypothesize that participants in the individualized intervention group will demonstrate greater improvements in burnout and well-being outcomes than those in the nonindividualized group. CONCLUSIONS: The findings of this study could guide the development of burnout prevention and well-being initiatives for medical students. Identifying PF as a mediating process would provide support for the delivery of preventive intervention programs that train individuals to strengthen this psychological resource before burnout symptoms emerge. This would be an important step in addressing and potentially offsetting the significant costs of burnout among medical students and physicians. Demonstrating the superiority of an individualized version of the app over a nonindividualized version would have implications for enhancing intervention precision and efficacy by using scalable interventions. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ANZCTR 12621000911897; https://www.anzctr.org.au/ACTRN12621000911897.aspx. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/32992.
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.068 | 0.011 |
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".