Facilitators and barriers for implementing screening brief intervention and referral for health promotion in a rural hospital in Alberta: using consolidated framework for implementation research
Bibliographic record
Abstract
Abstract Background Screening, brief intervention, and referral (SBIR) is an evidence-based, comprehensive health promotion approach commonly implemented to reduce alcohol and substance use. Implementation research on SBIR demonstrate that patients find it acceptable, reduces hospital costs, and it is effective. However, SBIR implementation in hospital settings for multiple risk factors (fruit and vegetable consumption, physical activity, alcohol and tobacco use) is still emergent. More evidence is needed to guide SBIR implementation for multiple risk factors in hospital settings. Objective To explore the facilitators and barriers of SBIR implementation in a rural hospital using the Consolidated Framework for Implementation Research (CFIR). Methods We conducted a descriptive qualitative investigation consisting of both inductive and deductive analyses. We conducted virtual, semi-structured interviews, guided by the CFIR framework. All interviews were audio-recorded, and transcribed verbatim. NVivo 12 Pro was used to organize and code the raw data. Results A total of six key informant semi-structured interviews, ranging from 45 to 60 min, were carried out with members of the implementation support team and clinical implementers. Implementation support members reported that collaborating with health departments facilitated SBIR implementation by helping (a) align health promotion risk factors with existing guidelines; (b) develop training and educational resources for clinicians and patients; and (c) foster leadership buy-in. Conversely, clinical implementers reported several barriers to SBIR implementation including, increased and disrupted workflow due to SBIR-related documentation, a lack of knowledge on patients’ readiness and motivation to change, as well as perceived patient stigma in relation to SBIR risk factors. Conclusion The CFIR provided a comprehensive framework to gauge facilitators and barriers relating to SBIR implementation. Our pilot investigation revealed that future SBIR implementation must address organizational, clinical implementer, and patient readiness to implement SBIR at all phases of the implementation process in a hospital.
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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 | 0.079 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.007 |
| 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".