Building competency in hematopoietic stem cell transplant coordination: Evaluating the effectiveness of a learning pathway for nurses new to this role
Bibliographic record
Abstract
There has been an increased need for highly skilled nursing staff trained in hematopoietic stem cell transplant (HSCT) care driven by the increase in transplant eligible patients (CCO, 2017). There is a lack of literature pertaining to orientation to HSCT coordination, a highly specialized nursing role. Historically, orientation to HSCT coordination has been preceptor based without a formal orientation process. Objectives: For this pilot study, a learning pathway and educational tools were developed and evaluated to support a standardized and systematic approach to the staff education and to improve quality of care. Methods: Eight nurses new to HSCT coordination participated in this study and completed the follow-up evaluations. Nurses were assessed before the intervention to identify knowledge gaps for each sub-role of allogeneic and autologous stem cell coordination. Following the assessment, nurses received a tailored self-directed learning package mapped to a learning pathway and a competency evaluation tool. A competency-based "building-block" training approach was used in which a new skill was added only after a previous skill was mastered. Utilizing the Kirkpatrick (2006) model, nurses were evaluated pre- and postorientation to assess changes in both knowledge and in behaviour relating to transplant coordination. Results: Participants reacted favourably to these tools and reported a significantly higher level of knowledge and competency in the transplant coordination role following orientation. Further research on the use of a learning pathway to guide the orientation of nurses to the HSCT coordination role would complement this pilot study.
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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".