Integrating an evidence‐based intervention into clinical practice: ‘transitional relationship model’
Bibliographic record
Abstract
Accessible summary The transitional relationship model (TRM) facilitates the discharge of psychiatric clients from hospital to community by providing hospital staff involvement until a therapeutic relationship has been established with a community care provider as well as peer support. Psychiatric wards at six hospital sites implemented the TRM in three waves. Monthly summaries, progress reports, meeting minutes and focus group discussions were reviewed in order to uncover facilitators and barriers to TRM implementation. Factors that facilitated TRM implementation included: educational modules for staff and peer training, the presence of on‐site champions, and supportive documentation systems. Barriers included: feeling swamped/overwhelmed, death by process, team dynamics and changes in champions. Implementation strategies suggested by the initial hospital wards were used to enhance implementation on subsequent wards, leading to positive outcomes. This study highlights the need to address work environment issues when implementing healthcare interventions, particularly for interprofessional teams. Abstract The transitional relationship model (TRM) facilitates the discharge process by providing peer support and hospital staff involvement until a therapeutic relationship has been established with a community care provider. A quasi‐experimental, action‐oriented research design was employed in which psychiatric wards at six hospital sites implemented the model in three waves. Helpful strategies were identified by each wave of wards for consideration by subsequent wards. Using an ethnographic approach, qualitative data were examined to uncover experiences and perceptions of TRM implementation and to help identify key issues that were supporting or hampering implementation. Specific strategies that facilitate the implementation of TRM include: (1) the use of educational modules for on‐ward hospital staff training and peer training; (2) presence of on‐site champions; and (3) supportive documentation systems. Issues identified as barriers to implementation included: (1) feeling drowned, swamped and overwhelmed; (2) death by process; (3) team dynamics; and (4) changes in champions. Staged large‐scale implementation of the TRM allowed for iterative improvements to the model leading to positive outcomes. This study highlights the need to address work environment issues, particularly interprofessional teams.
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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 | 0.046 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".