The role of mental health nursing in pediatric hematology/oncology – Part 2: Developing an implementation strategy for an innovative practice
Bibliographic record
Abstract
Objective: The objective of this article is to describe the process of implementing and performing a preliminary assessment of an innovative mental health nursing practice in a pediatric hematology/oncology department. The preliminary assessment will help us determine whether the implementation strategy used was appropriate. Background: Pediatric cancers present patients and their families with many difficulties that can sometimes give rise to mental health issues during and after treatment. A mental health nurse clinician with expertise in hematology/oncology could help patients and families experiencing such issues by providing care that complements currently available psychosocial services. As implementing changes in practice is often challenging, and introducing a role without a clear plan can lead to failure, we decided it was important to develop a clear implementation strategy. Our project, which involves creating the role of a mental health nurse clinician (MHNC) in pediatric hematology/oncology, is divided into three stages: (1) development of the MHNC role, (2) development of an implementation strategy and (3) evaluation of role feasibility, acceptability and appreciation one year after implementation. In this article, we discuss the second stage. The objectives of this article are to (1) present the strategy involved in implementing the role of the MHNC and (2) report on the findings of a preliminary post-implementation assessment of the feasibility, acceptability and appreciation of the role for exploratory purposes. Methodology: The implementation strategy we developed was based on the approach proposed by Fry and Rogers (2009) and adapted to the context of our project. The five steps of the approach are: (1) the communication strategy, (2) the consultative process to define the role and scope of practice, (3) education, (4) the establishment of a support structure and (5) assessment and feedback mechanisms. This last step took the form of a preliminary exploratory assessment, which we performed by administering a survey to nurses, specialized nurse practitioners, and doctors three months after the MHNC role was implemented. Results: In the results section, we present our detailed five-step implementation plan. The preliminary results of the survey administered three months following implementation indicate that the MHNC worked an average of 150 hours a month and carried out 22.7 consultations and 52.3 follow-ups per month, the average length of which was 53.1 minutes. In all, 96% of the healthcare professionals who responded to the survey said that someone with this type of expertise is needed in pediatric hematology/oncology, and only 28% said that they themselves felt they had the knowledge and skills required to effectively manage the medications used to treat mental health disorders. Conclusion: Our results seem to confirm the feasibility, acceptability, and appreciation of the MHNC role, suggesting that the implementation strategy proposed by Fry and Rogers (2009) is well suited to the development of a mental health nursing practice in pediatric hematology/oncology. In the third stage of this project, we will carry out a structured, rigorous assessment of the role’s feasibility, acceptability, and appreciation by patients, families, and healthcare professionals one year after implementation.
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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 | 0.056 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".