Identifying the Needs of Health Care Providers in Advanced First-Line Renal Cell Carcinoma: A Mixed-Methods Research
Bibliographic record
Abstract
INTRODUCTION: Systemic treatments for metastatic or unresectable renal cell carcinoma (mRCC) are rapidly evolving. This study aimed at investigating challenges in the care of mRCC to inform future educational interventions for health care providers (HCPs). MATERIALS AND METHODS: The sequential mixed-method design consisted of a qualitative phase (semistructured interviews) followed by a quantitative phase (online surveys). Participants included US-based medical oncologists, nephrologists, physician assistants, nurse practitioners, and registered nurses. Interview transcripts were thematically analyzed. Survey data was descriptively and inferentially analyzed. RESULTS: Forty interviews and 265 surveys were completed. Analysis revealed four challenges in the care of mRCC patients. A challenge in staying current with emerging evidence and treatment recommendations was found with 33% of surveyed HCPs reporting suboptimal skills interpreting published evidence on the efficacy and safety of emerging agents. A challenge weighing patient health and preferences in treatment decisions was found, especially among HCPs with 3 to 10 years of practice (37%) who reported suboptimal skills in assessing patients' tolerance to side effects. Promoting a collaborative care approach to the management of immune-related adverse events was a challenge, specifically related to barriers involving nephrologists (eg, diverging treatment goals). Breakdowns in communication were reported (46% of HCPs), especially in the monitoring of side effects and treatment adherence. CONCLUSION: This study revealed key challenges faced by HCPs when treating and managing patients with mRCC across multiple providers. Future interventions (eg, community of practice) should aim to address the identified gaps and promote a team-based approach to care that strengthens the complementary competencies of HCPs involved.
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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 | 0.049 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".