The effectiveness of generic emails versus a remote knowledge broker to integrate mood management into a smoking cessation programme in team-based primary care: a cluster randomised trial
Bibliographic record
Abstract
BACKGROUND: Knowledge brokering is a knowledge translation approach that has been gaining popularity in Canada although the effectiveness is unknown. This study evaluated the effectiveness of generalised, exclusively email-based prompts versus a personalised remote knowledge broker for delivering evidence-based mood management interventions within an existing smoking cessation programme in primary care settings. METHODS: The study design is a cluster randomised controlled trial of 123 Ontario Family Health Teams participating in the Smoking Treatment for Ontario Patients programme. They were randomly allocated 1:1 for healthcare providers to receive either: a remote knowledge broker offering tailored support via phone and email (group A), or a generalised monthly email focused on tobacco and depression treatment (group B), to encourage the implementation of an evidence-based mood management intervention to smokers presenting depressive symptoms. The primary outcome was participants' acceptance of a self-help mood management resource. The secondary outcome was smoking abstinence at 6-month follow-up, measured by self-report of smoking abstinence for at least 7 previous days. The tertiary outcome was the costs of delivering each intervention arm, which, together with the effectiveness outcomes, were used to undertake a cost minimisation analysis. RESULTS: Between February 2018 and January 2019, 7175 smokers were screened for depression and 2765 (39%) reported current/past depression. Among those who reported current/past depression, 29% (437/1486) and 27% (345/1277) of patients accepted the mood management resource in group A and group B, respectively. The adjusted generalised estimating equations showed that there was no significant difference between the two treatment groups in patients' odds of accepting the mood management resource or in the patients' odds of smoking abstinence at follow-up. The cost minimisation analysis showed that the email strategy was the least costly option. CONCLUSIONS: Most participants did not accept the resource regardless of remote knowledge broker strategy. In contexts with an existing KT infrastructure, decision-makers should consider an email strategy when making changes to a programme given its lower cost compared with other strategies. More research is required to improve remote knowledge broker strategies. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03130998 . Registered April 18, 2017, (Archived on WebCite at www.webcitation.org/6ylyS6RTe ).
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".