Learning from Canadian Stroke Rehabilitation Care Clinicians: Implications for the Management of Patient Complexity
Notice bibliographique
Résumé
Introduction: Clinicians working in stroke rehabilitation settings care for a high proportion of patients with complex care needs. The average patient recovering from a stroke has a minimum of two co-morbidities and a range of psychological and socio-economic needs (Gallacher et al., 2019; Schaink et al., 2014). The care trajectories of these “complex” patients usually deviate from standard practices and they require customized care. There is limited evidence to guide clinician decision-making in relation to complex patients, as their needs are not fully reflected in clinical practice guidelines (Boyd et al., 2005). In lieu of evidence, clinicians may engage in collaborative problem solving to generate innovative solutions (Nelson et al., 2016). However, implementing customized approaches may be difficult in environments that prioritize adherence to specific clinical pathways. For example, Stroke Distinction sites across Canada are recognized for delivering care in accordance with the Canadian Stroke Best Practice Recommendations (CSBPR) (Accreditation Canada, 2021). We sought to explore how expert stroke rehabilitation clinicians provide customized care to a large subset of complex patients, while meeting organizational performance requirements at Stroke Distinction sites. Methods: We used an interpretive descriptive research design (Thorne, 2016) to explore the research question: How do expert clinicians at Stroke Distinction sites recognize and manage the care of patients with complex care needs? We interviewed 16 clinicians (including medicine, allied health, nursing), four organizational leaders and two health system experts. We collected data via 45–60-minute virtual interviews and engaged in a hybrid inductive- deductive approach to analysis. Results: We reported three themes: (1) recognizing complexity is routine work for clinicians, (2) clinicians use workarounds to manage complexity, and (3) clinicians perceived and worked to bridge a difference between organizational processes and the realities of patient care. We noted differences regarding perceptions of patient complexity across participant types. For example, clinicians reported most of their patients to have complex care needs. They described care for patients with a high degree social complexity (e.g., limited family or financial support) as particularly difficult to manage. When unable to secure outcomes that patients “deserve”, clinicians reported experiencing moral distress. In contrast, the organizational and system experts described that stroke programs are designed for approximately 20% of patients to have complex care needs; this represents a large mismatch in perceptions of patient complexity in comparison with the clinician group. Conclusions and Implications: Expert clinicians use adaptive strategies to continually manage care for a high proportion of patients with complex care needs. However, they often report moral distress when these strategies are unable to compensate for health system limitations. Given the significant mismatch in perceptions of patient complexity between clinicians and leaders who shape systems of care, decision-makers could consider macro- and meso-level strategies to support the adaptive practices of clinicians in alignment with workforce strategies to prioritize the clinician retention. Next Steps: This research was a part of a doctoral dissertation. We continue to share this work and seek collaboration with others in supporting clinician management of complexity.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,084 | 0,192 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,008 |
| Études des sciences et des technologies | 0,054 | 0,028 |
| Communication savante | 0,023 | 0,011 |
| Science ouverte | 0,008 | 0,020 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
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 ».