Effects of a Person-Centered eHealth Intervention for Patients on Sick Leave Due to Common Mental Disorders (PROMISE Study): Open Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Sick leave due to common mental disorders (CMDs) is a public health problem in several countries, including Sweden. Given that symptom relief does not necessarily correspond to return to work, health care interventions focusing on factors that have proven important to influence the return to work process, such as self-efficacy, are warranted. Self-efficacy is also a central concept in person-centered care. OBJECTIVE: The aim of this study is to evaluate the effects of a person-centered eHealth intervention for patients on sick leave due to CMDs. METHODS: A randomized controlled trial of 209 patients allocated to either a control group (107/209, 51.2%) or an intervention group (102/209, 48.8%) was conducted. The control group received usual care, whereas the intervention group received usual care with the addition of a person-centered eHealth intervention. The intervention was built on person-centered care principles and consisted of telephone support and a web-based platform. The primary outcome was a composite score of changes in general self-efficacy (GSE) and level of sick leave at the 6-month follow-up. An intention-to-treat analysis included all participants, and a per-protocol analysis consisted of those using both the telephone support and the web-based platform. RESULTS: At the 3-month follow-up, in the intention-to-treat analysis, more patients in the intervention group improved on the composite score than those in the control group (20/102, 19.6%, vs 10/107, 9.3%; odds ratio [OR] 2.37, 95% CI 1.05-5.34; P=.04). At the 6-month follow-up, the difference was no longer significant between the groups (31/100, 31%, vs 25/107, 23.4%; OR 1.47, 95% CI 0.80-2.73; P=.22). In the per-protocol analysis, a significant difference was observed between the intervention and control groups at the 3-month follow-up (18/85, 21.2%, vs 10/107, 9.3%; OR 2.6, 95% CI 1.13-6.00; P=.02) but not at 6 months (30/84, 35.7%, vs 25/107, 23.4%; OR 1.8, 95% CI 0.97-3.43; P=.06). Changes in GSE drove the effects in the composite score, but the intervention did not affect the level of sick leave. CONCLUSIONS: A person-centered eHealth intervention for patients on sick leave due to CMDs improved GSE but did not affect the level of sick leave. TRIAL REGISTRATION: ClinicalTrials.gov NCT03404583; https://clinicaltrials.gov/ct2/show/NCT03404583.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".