Implementation strategies to address barriers to evidence-informed symptom management among outpatient oncology nurses: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Despite the availability of clinical practice guidelines for cancer symptom management, cancer care providers do not consistently use them in practice. Oncology nurses in outpatient settings are well positioned to use established guidelines to inform symptom assessment and management; however, issues concerning inconsistent implementation persist. This scoping review aims to (1) identify reported barriers and facilitators influencing symptom management guideline adoption, implementation and sustainability among specialised and advanced oncology nurses in cancer-specific outpatient settings and (2) identify and describe the components of strategies that have been used to enhance the implementation of symptom management guidelines. METHODS AND ANALYSIS: This scoping review will follow Joanna Briggs Institute methodology. Electronic databases CINAHL, Embase, Emcare and MEDLINE(R) and grey literature sources will be searched for studies published in English from January 2000 to March 2022. Primary studies and grey literature reports of any design that include specialised or advanced oncology nurses practicing in cancer-specific outpatient settings will be eligible. Sources describing factors influencing the adoption, implementation and sustainability of cancer symptom management guidelines and/or strategies to enhance guideline implementation will be included. Two reviewers will independently screen for eligibility and extract data. Data extraction of factors influencing implementation will be guided by the Consolidated Framework for Implementation Research (CFIR), and the seven dimensions of implementation strategies (ie, actors, actions, targets, temporality, dose, justifications and outcomes) will be used to extract implementation strategy components. Factors influencing implementation will be analysed descriptively, synthesised according to CFIR constructs and linked to the Expert Recommendations for Implementating Change strategies. Results will be presented through tabular/diagrammatic formats and narrative summary. ETHICS AND DISSEMINATION: Ethics approval is not required for this scoping review. Planned knowledge translation activities include a national conference presentation, peer-reviewed publication, academic social media channels and dissemination within local oncology nursing and patient networks.
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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 | 0.144 | 0.111 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.010 |
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".