Trial staff views on barriers recruitment in a digital intervention for psychosis and how to work around them: A qualitative study within a trial
Bibliographic record
Abstract
Abstract Background: Recruitment processes for clinical trials of digital interventions for psychosis are seldom described in detail within the literature. While trial staff have expertise in describing barriers and facilitators to recruitment a specific focus on understanding recruitment from the point of view of trial staff is rare.Methods: We applied pluralistic ethnographic methods including analysis of trial documents, observation and focus groups explored the recruitment processes of the EMPOWER feasibility trial (ISRCTN: 99559262).Results: Recruitment barriers fell into two main themes; service characteristics (lack of time available to mental health staff to support recruitment, staff turnover, patient turnover (within Australia only), management styles of community mental health teams, physical environment) and clinician expectations (filtering effects and resistance to research participation). Trial staff negotiated these barriers through strategies such as emotional labour (trial staff managing feelings and expressions in order to successfully recruit participants) and trying to build relationships with clinical staff working within community mental health teams.Conclusions: Researchers in clinical trials for digital psychosis interventions face numerous recruitment barriers and do their best to work flexibly negotiate these barriers and meet recruitment targets. The recruitment process appeared to be enhanced by trial staff supporting each other throughout the recruitment stage of the trial.Trial Registration: (ISRCTN: 99559262 registered 21/12/2015)
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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 | 0.120 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".