Contextual analysis for the implementation of a digital psychosocial intervention to enhance mental health outcomes in North Macedonia
Bibliographic record
Abstract
Introduction This study represents the first effort in North Macedonia to examine the contextual attributes that may influence the effectiveness and acceptability of a novel digital intervention, DIALOG+, within the mental health care system. The intervention aims to enhance mental health outcomes through a structured approach, but its success depends on understanding the specific characteristics of the local health context. Objectives The primary objective of this research is to identify the key contextual attributes within the mental health care system of North Macedonia that are relevant to the successful implementation of DIALOG+. This includes examining factors that could impact both the effectiveness of the intervention and its acceptance by various stakeholders, including patients, clinicians, carers, and policymakers. Methods Data for this study were drawn from a variety of sources, including the National Mental Health Strategy 2018-2025, relevant documents from the World Health Organization, and other action plans. In addition, interviews were conducted with key stakeholders—patients, carers, clinicians, and policymakers—to gather perspectives on the anticipated introduction of DIALOG+ and assess the readiness of the mental health centers for its implementation. The data were subsequently mapped to a framework developed by the Ottawa Implementation Group, which outlines 14 key contextual attributes influencing health interventions. Results The findings were categorized into two subgroups, identifying both facilitators and barriers to the implementation of DIALOG+ in North Macedonia’s mental health system. The intervention’s characteristics as a broadly applicable psychosocial tool align well with modern approaches to psychosocial rehabilitation, particularly for individuals diagnosed with psychosis. Conclusions DIALOG+ presents a valuable tool for mental health professionals in North Macedonia, offering structured support for monitoring patient progress and achieving institutional objectives. The intervention has the potential to facilitate patients’ reintegration into society, enhance their independence, and enable them to reach their full potential in the pursuit of a healthy and functional life. Disclosure of Interest None Declared
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".