Développement et tests utilisateurs de l’application Web PRATICAdr : Plateforme de Retour Au Travail axée sur les Interactions et la Communication entre les Acteurs, intégrant un programme Durable favorisant le Rétablissement
Bibliographic record
Abstract
Introduction Common mental disorders (CMDs) account for 30% to 50% of all illness absences. The success of RTW is not only due to the individual but rather to the result of the interaction between the stakeholders involved in the RTW process. Numerous mental health applications have been developed to improve patient management and optimize communication between professionals, but have not been validated. Moreover, no technological solution has been developed to date to facilitate both consultation among the RTW stakeholders (e.g., managers, health professionals) and systematic support for the employee in his or her RTW. Objective To address these shortcomings, the purpose of this article is twofold: 1) to describe the development of the PRATICAdr application (Return-to-Work Platform focused on Stakeholder Interaction and Communication: a Sustainable Recovery Program) and 2) to document PRATICAdr application user testing. Method The development of PRATICAdr has been operationalized in three phases: 1) needs assessment, 2) conceptualization of the internal mechanisms of the application and programming techniques and 3) testing of the application in real situation. The application is evaluated through questionnaires and interviews to measure user satisfaction. Results PRATICAdr allows to follow in real time the path of RTW stakeholders involved in the personalized support of the employee in his RTW. The operationalization of the RTW process and the inclusion of validated assessment tools help systematize the stakeholders' consultation and shared decision-making, as well as the monitoring and actions taken to undertake a recovery-promoting RTW. The PRATICAdr interface was developed to simplify the user experience for the employee on sick leave and all RTW stakeholders. Regarding user satisfaction, results show that the first 16 users of PRATICAdr, employees in a large healthcare organization returning to work following a CMD, were very satisfied (average>9/10) with the Web application, as well as the participation of RTW stakeholders and the questionnaires included in PRATICAdr. Improvements were also suggested. Conclusion PRATICAdr is implemented in two large organizations (>15,000 employees) in order to evaluate its effectiveness with employees on sick leave due to CMD registered in a RTW process. The aim of this article was to present not only the development of PRATICAdr, but also to measure user satisfaction. Preliminary results indicate a high level of satisfaction among employees on sick leave who used PRATICAdr. In terms of future avenues, the integration of e-learning will be addressed with the objective of customizing the RTW program according to the predictions of duration of sick leave and sustainable RTW.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".