The impact of Stress Management and Resilience Training (SMART) on academic physicians during the implementation of a new Health Information System: An exploratory randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: The Stress Management and Resilience Training (SMART) program is an evidence-based intervention designed to build resilience in physicians in clinical practice. The objective of the current study was to assess the impact of the SMART program on academic physicians' levels of resilience, subjective happiness, stress, and anxiety, and specifically during the implementation of a new hospital-wide Health Information System (HIS). METHODS: A total of 40 physicians in a tertiary care academic hospital were randomized (allocation ratio 1:1) to either the SMART intervention or the control condition. The SMART intervention consisted of one mandatory two-hour in-person workshop and an optional 24-week online program, designed to support the materials delivered in the workshop. Outcome measures were assessed using validated scales administered online at baseline and at 3-months and 6-months follow-up. RESULTS: After adjusting for baseline levels of each outcome, no statistically significant intervention effect was observed for resilience, subjective happiness, stress or anxiety at 3-months or 6-months follow-up. However, physicians in the intervention group demonstrated improvements in resilience, stress and anxiety at follow-up that were within the range of clinically relevant differences. CONCLUSIONS: The findings of this exploratory study provide modest support that the SMART intervention may be beneficial for proactively addressing physician wellness during the implementation of a new HIS and that larger randomized trials are warranted. TRIAL REGISTRATION: NCT04384861.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".