Barriers and facilitators to the implementation of an early intervention in psychosis service in three demonstration sites in Ireland
Bibliographic record
Abstract
BACKGROUND: Programmes for early intervention (EIP) in psychosis for people experiencing a first episode of psychosis (FEP) have been found to be both clinically and cost effective. Following the publication of a new EIP model of care (MoC) in Ireland, the aim of this research is to describe how people participated in and responded to the MoC including service users, family members, HSE clinical staff and HSE management. METHODS: Qualitative design using the UK Medical Research Council's process evaluation framework. Purposive sampling techniques were used. A total of N = 40 key informant semi-structured interviews were completed which included clinical staff (N = 22), health service managers and administrators (N = 9), service users (N = 8) and a family member (N = 1). Thematic analyses were conducted. RESULTS: Unique features of the EIP service (e.g., speed of referral/assessment, multidisciplinary approach, a range of evidence-based interventions and assertive MDT follow up) and enthusiasm for EIP were identified as two key factors that facilitated implementation. In contrast, obstacles to staff recruitment and budget challenges emerged as two primary barriers to implementation. CONCLUSIONS: The findings from this research provide real world insights into the complexity of implementing an innovative service within an existing health system. Clear and committed financial and human resource processes which allow new innovations to thrive and be protected during their initiation and early implementation phase are paramount. These elements should be considered in the planning and implementation of EIP services both nationally in Ireland and internationally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".