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Record W1978932943 · doi:10.1375/jsc.5.1.57

Explorations of Self-Efficacy: Personal Narratives as Qualitative Data in the Analysis of Smoking Cessation Efforts

2010· article· en· W1978932943 on OpenAlexafffund
Erin Rollins, Jenepher Lennox Terrion

Bibliographic record

VenueThe Journal of Smoking Cessation · 2010
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa Heart Institute FoundationUniversity of Ottawa
KeywordsSmoking cessationNarrativeAddictionIntervention (counseling)Self-efficacyPsychologyQuit smokingQualitative researchClinical psychologySocial psychologyMedicinePsychiatrySociologySocial science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.083
GPT teacher head0.393
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2010
Admission routes2
Has abstractyes

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