Organizational Readiness for Implementing an Internet-Based Cognitive Behavioral Therapy Intervention for Depression Across Community Mental Health Services in Albania and Kosovo: Directed Qualitative Content Analysis
Bibliographic record
Abstract
BACKGROUND: The use of digital mental health programs such as internet-based cognitive behavioral therapy (iCBT) holds promise in increasing the quality and access of mental health services. However very little research has been conducted in understanding the feasibility of implementing iCBT in Eastern Europe. OBJECTIVE: The aim of this study was to qualitatively assess organizational readiness for implementing iCBT for depression within community mental health centers (CMHCs) across Albania and Kosovo. METHODS: We used qualitative semistructured focus group discussions that were guided by Bryan Weiner's model of organizational readiness for implementing change. The questions broadly explored shared determination to implement change (change commitment) and shared belief in their collective capability to do so (change efficacy). Data were collected between November and December 2017. A range of health care professionals working in and in association with CMHCs were recruited from 3 CMHCs in Albania and 4 CMHCs in Kosovo, which were participating in a large multinational trial on the implementation of iCBT across 9 countries (Horizon 2020 ImpleMentAll project). Data were analyzed using a directed approach to qualitative content analysis, which used a combination of both inductive and deductive approaches. RESULTS: Six focus group discussions involving 69 mental health care professionals were conducted. Participants from Kosovo (36/69, 52%) and Albania (33/69, 48%) were mostly females (48/69, 70%) and nurses (26/69, 38%), with an average age of 41.3 years. A directed qualitative content analysis revealed several barriers and facilitators potentially affecting the implementation of digital CBT interventions for depression in community mental health settings. While commitment for change was high, change efficacy was limited owing to a range of situational factors. Barriers impacting "change efficacy" included lack of clinical fit for iCBT, high stigma affecting help-seeking behaviors, lack of human resources, poor technological infrastructure, and high caseload. Facilitators included having a high interest and capability in receiving training for iCBT. For "change commitment," participants largely expressed welcoming innovation and that iCBT could increase access to treatments for geographically isolated people and reduce the stigma associated with mental health care. CONCLUSIONS: In summary, participants perceived iCBT positively in relation to promoting innovation in mental health care, increasing access to services, and reducing stigma. However, a range of barriers was also highlighted in relation to accessing the target treatment population, a culture of mental health stigma, underdeveloped information and communications technology infrastructure, and limited appropriately trained health care workforce, which reduce organizational readiness for implementing iCBT for depression. Such barriers may be addressed through (1) a public-facing campaign that addresses mental health stigma, (2) service-level adjustments that permit staff with the time, resources, and clinical supervision to deliver iCBT, and (3) establishment of a suitable clinical training curriculum for health care professionals. TRIAL REGISTRATION: ClinicalTrials.gov NCT03652883; https://clinicaltrials.gov/ct2/show/NCT03652883.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".