Designing the Well-Being of Romanians by Achieving Mental Health with Digital Methods and Public Health Promotion
Bibliographic record
Abstract
Taking care of mental health is a state of mind. Amid the challenges of the current context, mental health has become one of the problems with the greatest impact on citizens and the evolution of any economy. Due to the COVID-19 pandemic, people have become more anxious, solitary, preoccupied with themselves, and depressed because their entire universe has changed, by restricting their social and professional life; the increase in concern caused by a possible illness of them or those close to them made to isolate themselves. Two qualitative (group and in-depth interviews) and one survey-based quantitative research were carried out, which allowed the quantification of the opinions, perceptions, and attitudes of Romanians regarding the effectiveness of policies for the prevention and treatment of depression. Quantitative research revealed that most of the subjects had never participated in a mental health assessment, and a quarter of them had visited a mental health specialist more than two years ago. Based on the results, proposals were elaborated, which have been addressed both to the specialists from the Ministry of Health and to those from the academic environment, that may have an impact on the elaboration of some public mental health programs.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".