Making homes smoke-free : the impact of an empowerment intervention for parents
Bibliographic record
Abstract
One-third of American children under the age of 18 years and one in ten Canadian children aged 0-11 years are exposed to environmental tobacco smoke (ETS) predisposing them to multiple health problems. Although several intervention strategies to reduce ETS exposure among children have been tested, to date there is not enough evidence to recommend one strategy over another. The objectives of this study were: (a) to test if parents' participation in an intervention based on an empowerment ideology and participatory experiences decreases the number of cigarettes smoked in homes; and (b) to identify barriers to making homes and vehicles smoke-free, as well as facilitators used by parents to manage these barriers. To enable informed decision-making on how to measure empowerment, a systematic review was conducted to identify questionnaires that best measure health-related empowerment among adults and in families. In a randomized controlled trial, 36 families were allocated to the intervention (n=17) or control group (n=19). The six week intervention included three, two hour group sessions, followed by three follow-up telephone calls, all at weekly intervals. Data were collected in interviewer-administered questionnaires at baseline and at six months follow-up. No significant difference was detected between the intervention and control groups in the number of cigarettes smoked in the home daily at six months follow-up. However empowerment increased and the number of cigarettes smoked in the home decreased in both the intervention and control groups from baseline (median=17) to six-month follow-up (median=5). Parents identified multiple barriers to smoke-free homes and vehicles including personal factors, factors involving others, and factors related to the physical environment. The most commonly identified barriers to smoke-free homes were personal factors, with tobacco addiction cited most often. In describing how to overcome barriers, parents identified facilitators involving other people as most effective, yet they most often relied on themselves. None ofthe parents identified a health provider as a facilitator. The multiple and complex barriers identified in this study suggest that interventions and practice guidelines should incorporate multiple strategies and individualized approaches to assist parents to make their homes and vehicles smoke-free.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".