Reducing delay in patients with acute coronary syndrome and other time-critical conditions: a systematic review to identify the behaviour change techniques associated with effective interventions
Bibliographic record
Abstract
Background: Time to treatment in many conditions, particularly acute coronary syndrome, is critical to reducing mortality. Delay between onset of symptoms and treatment remains a worldwide problem. Reducing patient delay has been particularly challenging. Embedding behaviour change techniques (BCTs) within interventions might lead to shorter delay. Objective: To identify which BCTs are associated with reductions in patient delay among people with symptoms or conditions where time to treatment is critical. Methods: The data sources were Cochrane Library, MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature, and PsycINFO. Study eligibility criteria include intervention evaluations (randomised controlled trials, controlled clinical trials and cohort studies) involving adults (aged >18 years) and including an outcome measure of patient delay up to August 2016. Study appraisal and synthesis methods include screening potential studies using a transparent, replicable process. Study characteristics, outcomes and BCTs were extracted from eligible studies. Results: From 39 studies (200 538 participants), just over half (n=20) reported a significant reduction in delay. 19 BCTs were identified, plus 5 additional techniques, with a mean of 2 (SD=2.3) BCTs and 2 (SD=0.7) per intervention. No clear pattern between BCTs and effectiveness was found. In studies examining patient delay specifically, three of four studies that included two or more BCTs, in addition to the two most commonly used additional techniques, reported a significant reduction in delay. Conclusions: Around half of the interventions to reduce prehospital delay with time-critical symptoms report a significant reduction in delay time. It is not clear what differentiates effective from non-effective interventions, although in relation to patient delay particularly additional use of BCTs might be helpful. Trial registration number: CRD42014013106.
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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".