Establishing Barriers to and Enablers of Nurse-Enabled Subcutaneous Therapy Self-Administration Programs for Patients With Myeloma: Protocol for a Qualitative Descriptive Study
Bibliographic record
Abstract
Background: Multiple myeloma (MM) is associated with the greatest symptom burden of all hematological cancers and, despite substantial improvements in treatment options with high response and survival rates, is still considered incurable, with patients undergoing multiple lines of therapy over many years. Subcutaneous (SC) injections are a common mode of delivery for current and future MM therapy, with evidence suggesting that programs that give patients or carers responsibility for administration can bring benefits to the patient and health care system by reducing the number of required visits to hospital. Objective: This study will explore and describe barriers to and enablers of implementing nurse-enabled SC therapy self-administration programs for patients with MM to develop a road map for national scalability. Methods: This qualitative descriptive study is informed by the Consolidated Framework for Implementation Research. Participants included key stakeholders from across Australia, including patients, carers, health professionals, and policymakers with experience of implementation, facilitation, and participation in nurse-enabled SC therapy self-administration programs. Data were collected via virtual focus groups or semistructured interviews and analyzed using the framework method to identify barriers and enablers. The Expert Recommendations for Implementing Change matching tool will be used to develop strategies to target barriers and enhance enablers, informing the development of a national road map. Results: This study was funded in March 2024 and approved by Peter MacCallum Cancer Centre Human Research Ethics Committee in May 2024. Data collection was conducted between June 2024 and November 2024. A total of 32 participants were recruited. Data analysis is underway, with results expected to be published in February 2026. Conclusions: To our knowledge, this study will be the first of its kind to identify and compare barriers to and enablers of implementing nurse-enabled SC self-administration programs for patients with MM. Applying the Consolidated Framework for Implementation Research to guide study processes provides an evidence-informed approach to understanding how discrete and intersecting factors influence program implementation and sustainability, informing the development of a comprehensive implementation road map. As the availability of SC therapies grows for other cancers and chronic diseases, this model of care could serve as a blueprint for broader applications, impacting patient quality of life and optimization of health care use.
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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 | 0.055 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".