Identifying Educational Needs and Practice Gaps of European Hematologists and Hematology Nurses in the Treatment and Management of Multiple Myeloma
Bibliographic record
Abstract
This needs-assessment aimed to identify clinical challenges faced by hematologists and hematology nurses in the diagnosis, treatment, and management of multiple myeloma, as well as contextual barriers hindering optimal care of patients with multiple myeloma. This manuscript focuses on key findings in relation to decision-making regarding new treatment options. A mixed methods study consisting of qualitative (from semistructured interviews) and quantitative data (from an online survey) was conducted in 8 European countries among hematologists and hematology nurses. The triangulated data led to the identification of 3 key findings related to decision-making: (1) Educational needs regarding mechanisms of action and side effect profiles of new therapies, (2) educational needs regarding the sequencing and combination of new agents with current therapies, and (3) contextual barriers to the integration of new agents. Specific knowledge and skill gaps were identified as causalities of the educational needs, providing information to guide future educational programs. Of note, 34% of hematologists and 69% of nurses reported suboptimal knowledge of the mechanisms of action of new agents and 30% of hematologists reported suboptimal skills integrating new agents in combination with current treatments. This needs-assessment highlighted the importance to address the educational needs and their underlying causes through medical education activities to ensure hematologists and hematology nurses are up-to-date with the latest treatments in the field as they become available. The contextual barriers identified should be considered when designing the educational programs to ensure content is applicable to the clinical reality of learners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".