Implementation of a post-arrest care team: understanding the nuances of a team-based intervention
Bibliographic record
Abstract
BACKGROUND: Despite advances in the management of sudden cardiac arrest, mortality for patients admitted to hospital is still greater than 50 %. Lack of familiarity and experience with post-cardiac arrest patients and lack of interdisciplinary collaboration between emergency and ICU staff have been highlighted as potential barriers to optimal care. To address these barriers, a specialized Post Arrest Consult Team (PACT) was implemented at two urban academic centers. Our objective was to describe the PACT implementation from the participant perspective in order to explore potentially mitigating factors on effectiveness of the intervention and inform other institutions who may be considering a similar approach. METHODS: Using an ethnographic style approach, we collected data throughout the implementation period using both key informant interviews and non-participant observation. The data were analyzed using interpretive descriptive analysis techniques. RESULTS: The PACT intervention was taken up differently in each of the two participating institutions. Participants spoke about the difficulty in maintaining a dynamic interaction between the team members and a shared sense of purpose, the challenge of off-service consulting and the impact of the lack of data feedback to support whether the project was effecting change. CONCLUSIONS: It appears that purposefully creating a "sense of team," the team composition and organizational culture and provision of performance feedback are important facilitators to ensuring uptake of a team-based intervention like the PACT model. Reporting of the intervention design and actual implementation experience like we have done here is crucial to allow readers to judge the quality of the study, to properly replicate it, and to contemplate how various factors may influence the outcome of a complex intervention.
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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 | 0.075 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".