Medical Student Experiences of Engaging in a Psychological Flexibility Skill Training App for Burnout and Well-being: Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Medical students are at higher risk of burnout than the general population. Interventions that facilitate adaptive coping behaviors (eg, Psychological Flexibility) in the context of inherent stressors associated with medical training could mitigate burnout risk and improve well-being. Delivering these interventions using smartphone apps offers advantages such as accessibility, scalability, mitigation of time and stigma barriers, and facilitation of individual tailoring (individualization). There is a need for feasibility trials with medical students in this emerging field. Formal evaluations of user experiences of app-based psychological skill training are required to identify barriers to and facilitators of engagement and optimize intervention development before implementation in efficacy trials and real-world settings. OBJECTIVE: This study aimed to assess the feasibility of delivering an individualized Psychological Flexibility skill training intervention (Acceptance and Commitment Training [ACTraining]) to medical students using an app-based delivery format. We further aimed to explore how formal evaluation of user experiences might inform and guide the development of this app before implementation in an efficacy trial and future research involving app-delivered psychological skill training for medical students. METHODS: This single-arm study was an early-phase feasibility trial of a stand-alone ACTraining app conducted with a sample of Australian medical students (n=11). We collected app usability and user experience data across a broad range of domains (eg, perceived helpfulness and relevance, learning experiences, and self-efficacy) using self-report questionnaires (quantitative and qualitative) and behavioral engagement outcomes. RESULTS: Behavioral engagement data demonstrated that the app delivered the assessment procedures and individualized ACTraining intervention to medical students as intended. The subjective feedback provided by students who actively engaged with the app was generally positive across several indicators, including usability, perceived relevance and helpfulness, accessibility, maintenance of privacy, and opportunity for self-reflection. Disengagement from the app was an identified challenge throughout the trial. Participant feedback identified several factors that may have affected engagement, such as time, expectations regarding app interface functioning, and individual differences in confidence and self-efficacy when implementing skills. CONCLUSIONS: This study reports user experience data that have been largely absent from the literature on digital psychological interventions for medical students. Our findings demonstrate the preliminary feasibility of an app-delivered ACTraining intervention for medical student well-being and burnout and support the value of future assessment of the efficacy of this approach with larger samples. We consider subjective feedback from medical students in relation to observed engagement and propose how this information might be used to inform the development of this app and future research in this nascent field.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".