Implementing Facilitated Access to a Text Messaging, Smoking Cessation Intervention Among Swedish Patients Having Elective Surgery: Qualitative Study of Patients’ and Health Care Professionals’ Perspectives
Bibliographic record
Abstract
BACKGROUND: There is strong evidence that short-term smoking cessation before surgery can reduce postoperative morbidity. There are, however, several structural problems in health care systems concerning how to implement smoking cessation interventions in routine practice for preoperative patients. OBJECTIVE: This study aimed to analyze the implementation of a text messaging, smoking cessation intervention targeting patients having elective surgery. Implementation of facilitated access (ie, referral from practitioners) and the perceived usefulness among patients were investigated. Elective surgery is defined as scheduled, nonacute surgery. METHODS: A qualitative study was carried out at two medium-sized hospitals in the south of Sweden. The implementation of facilitated access was investigated during a 12-month period from April 2018 to April 2019. Facilitated access was conceptualized as specialists recommending the text messaging intervention to patients having elective surgery. Implementation was explored in terms of perceptions about the intervention and behaviors associated with implementation; that is, how patients used the intervention and how specialists behaved in facilitating usage among patients. Two focus groups with smoking cessation specialists and 10 individual interviews with patients were carried out. Qualitative content analysis was used to analyze the data. RESULTS: Two main categories were identified from the focus group data with smoking cessation specialists: implementation approach and perceptions about the intervention. The first category, implementation approach, referred to how specialists adapted their efforts to situational factors and to the needs and preferences of patients, and how building of trust with patients was prioritized. The second category, perceptions about the intervention, showed that specialists thought the content and structure of the text messaging intervention felt familiar and worked well as a complement to current practice. Two categories were identified from the patient interview data: incorporating new means of support from health care and determinants of use. The first category referred to how patients adopted and incorporated the intervention into their smoking cessation journey. Patients were receptive, shared the text messages with friends and family, humanized the text messages, and used the messages as a complement to other strategies to quit smoking. The second category, determinants of use, referred to aspects that influenced how and when patients used the intervention and included the following: timing of the intervention and text messages, motivation to change, and perceptions of the mobile phone medium. CONCLUSIONS: Smoking cessation specialists adopted an active role in implementing the intervention by adapting their approach and fitting the intervention into existing routines. Patients showed strong motivation to change and openness to incorporate the intervention into their behavior change journey; however, the timing of the intervention and messages were important in optimizing the support. A text messaging, smoking cessation intervention can be a valuable and feasible way to reach smoking patients having elective surgery.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".