Implementation Strategies to Support Evidence-Informed Symptom Management Among Outpatient Oncology Nurses: A Scoping Review Protocol
Bibliographic record
Abstract
Abstract Introduction: Despite the availability of clinical practice guidelines for cancer symptom management, cancer care providers do not consistently utilize 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 identify and describe the components of implementation strategies that have been used to enhance the adoption, implementation, and sustainability of symptom management guidelines among specialized and advanced oncology nurses in cancer-specific outpatient settings. Factors influencing guideline implementation will also be identified. Methods and analysis: This scoping review will follow Joanna Briggs Institute methodology. Electronic databases CINAHL, Embase, Emcare, MEDLINE(R), and grey literature sources will be searched for studies published in English since the year 2000. Primary studies and grey literature reports of any design that include specialized or advanced oncology nurses practicing in cancer-specific outpatient settings will be eligible. Sources describing implementation strategies to enhance the adoption, implementation, and sustainability of cancer symptom management guidelines and/or factors influencing implementation will be included. Two reviewers will independently screen for eligibility and extract data. Data extraction will be guided by the Consolidated Framework for Implementation Research (CFIR). Data will be analyzed descriptively and synthesized according to CFIR constructs. 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.
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 | 0.129 | 0.100 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.013 |
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".