Self-Reflected Well-Being via a Smartphone App in Clinical Medical Students: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Well-being in medical students has become an area of concern, with a number of studies reporting high rates of clinical depression, anxiety, burnout, and suicidal ideation in this population. OBJECTIVE: The aim of this study was to increase awareness of well-being in medical students by using a smartphone app. The primary objective of this study was to determine the validity and feasibility of the Particip8 app for student self-reflected well-being data collection. METHODS: Undergraduate medical students of the Dunedin School of Medicine were recruited into the study. They were asked to self-reflect daily on their well-being and to note what experiences they had encountered during that day. Qualitative data were also collected both before and after the study in the form of focus groups and "free-text" email surveys. All participants consented for the data collected to be anonymously reported to the medical faculty. RESULTS: A total of 29 participants (69%, 20/29 female; 31%, 9/29 male; aged 21-30 years) were enrolled, with overall median compliance of 71% at the study day level. The self-reflected well-being scores were associated with both positive and negative experiences described by the participants, with most negative experiences associated with around 20% lower well-being scores for that day; the largest effect being "receiving feedback that was not constructive or helpful," and the most positive experiences associated with around 20% higher scores for that day. CONCLUSIONS: The study of daily data collection via the Particip8 app was found to be feasible, and the self-reflected well-being scores showed validity against participant's reflections of experiences during that day.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; both teacher heads agree on what is shown here.
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".