Front-office staff can improve clinical tobacco intervention: health coordinator pilot project.
Bibliographic record
Abstract
OBJECTIVE: To learn whether front-line personnel in primary care practices can increase delivery of clinical tobacco interventions and also help smokers address physical inactivity, at-risk alcohol use, and depression. DESIGN: Uncontrolled before-and-after design. SETTING: Vancouver, BC, area (4 practices); northern British Columbia (2 practices). PARTICIPANTS: Six practices, with 1 staff person per practice serving as a "health coordinator" who tracked and, after the baseline period, delivered preventive interventions to all patients who smoked. To assess delivery of preventive interventions, each practice was to sample 300 consecutive patient records, both at baseline and at follow-up 15 months later. INTERVENTIONS: Front-office staff were recruited, trained, paid, and given ongoing support to provide preventive care. Clinicians supplemented this care with advice and guided the use of medication. MAIN OUTCOME MEASURES: Effectiveness of the intervention was based on comparison, at baseline and at follow-up, of the proportion of patients with any of the following 6 proven intervention components documented in their medical records: chart reminder, advice received, self-management plan, target quit date, referral, and follow-up date (as they applied to tobacco, physical inactivity, at-risk alcohol use, and depression). A Tobacco Intervention Flow Sheet cued preventive care, and its data were entered into a spreadsheet (which served as a smokers' registry). Qualitative appraisal data were noted. RESULTS: For tobacco, substantial increases occurred after the intervention period in the proportion of patients with each of the intervention components noted in their charts: chart reminder (20% vs 94%); provision of advice (34% vs 79%); self-management plan (14% vs 57%); target quit date (5% vs 11%); referral (6% vs 11%); and follow-up date (7% vs 42%). Interventions for physical inactivity and depression showed some gains, but there were no gains for at-risk alcohol use. Front-line staff, patients, and clinicians were enthusiastic about the services offered. CONCLUSION: Selected front-office personnel can substantially increase the delivery of evidence-based clinical tobacco intervention and increase patient and staff satisfaction in doing so. How far these findings can be generalized and their population effects require further study.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".