Examination of youth tobacco cessation needs, demand and services within the City of Greater Sudbury / by David R. Groulx.
Bibliographic record
Abstract
Tobacco use contributes to a significant amount of morbidity and mortality. \nYouth are interested in quitting smoking yet most often do not choose assisted cessation \napproaches which have been shown effective in the literature (CDC, 2006). Youth within \nthe City of Greater Sudbury (CGS) have indicated a desire to quit smoking however \nanecdotal evidence has pointed to the lack of services in the area (Groulx, 2005; Sudbury \n& District Health Unit [SDHU], 2004a). To better understand local interest and demand \nfor cessation services, analysis of a subset of data from the School Health Action, \nPlanning and Evaluation System (SHAPES) Ontario (tobacco module) survey was \ncompleted. Data were derived from one secondary school in the CGS yielding 589 \ncompleted questionnaires. Analysis included frequencies, cross tabulations, and logistic \nregressions and focused on identification of local youths preferred cessation methods, \nfactors most likely to impact youth success in smoking cessation, and factors influencing \nthe students' preference to use assisted tobacco cessation methods/aids. Similar to \nprevious studies which analyzed the provincial SHAPES dataset, most youth preferred to \nquit on their own. However, unlike previous literature, interest in, and factors \ncontributing to interest in some assisted cessation methods was demonstrated. Analysis \ninto the forces impacting the demand and provision of cessation services at the local level \nwas also conducted. Despite the great capacity at the local level, environmental factors \nand provincial forces impacting the demand for cessation are evident as is the lack of \nyouth specific cessation services. Recommendations for public health are proposed and \nare aimed at increasing demand for, and provision of evidence based cessation support \nfor youth.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".