Explorations of Self-Efficacy: Personal Narratives as Qualitative Data in the Analysis of Smoking Cessation Efforts
Bibliographic record
Abstract
Abstract Research has found that an individual's perceived self-efficacy, supported by goals and the acceptance of potential obstacles, has the ability to assist in behaviour modification. By examining the narratives of cardiovascular patients undergoing smoking cessation counselling, this study highlights factors that individuals communicate in their narratives regarding changes to self-efficacy throughout the process of smoking cessation. Narrative analysis is used to establish those factors that cardiovascular patients assert to be the motivating or impeding factors in their smoking cessation efforts, particularly in relation to their initial readiness to quit smoking. The study's findings illustrate the social, physical and psychological barriers and motivating factors that exist for cardiovascular patients in the process of quitting smoking. The current study supplements past research illustrating that in-hospital programs are among the most influential smoking intervention strategies because they can be tailored to each patient's specific health problems and personal and social circumstances. The study concludes that the relationship formed between patients and intervention specialists can assist in raising an individual's self-efficacy to end an addictive behaviour.
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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".