The role of mental health nursing in pediatric hematology/oncology – Part 2: Developing an implementation strategy for an innovative practice
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,056 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».