Participatory System Mapping of a Hospice Care System: Hybrid Design Workshops With Hospice Stakeholders (Preprint)
Notice bibliographique
Résumé
Background: Palliative and end-of-life care (PEoLC) systems are expanding across services, settings, and stakeholders, increasing their complexity and the need for systemic understanding to support patient outcomes and service delivery. Hospice care is central to the future of PEoLC, as hospices provide holistic services and engage diverse stakeholders. Participatory system mapping offers a way to collectively understand and visualize complex dynamics with those who live and work within these systems. Objective: This study aims to capture hospice system dynamics and preliminary leverage points via participatory causal loop diagram (CLD) mapping while evaluating method suitability through 3 research questions: (RQ1) What key variables and causal interrelationships do stakeholders identify in a hospice through participatory system mapping workshops? (RQ2) What preliminary leverage points emerge from the system map? and (RQ3) How effective are participatory system mapping workshops for capturing hospice dynamics? Methods: We developed and iteratively refined an innovative hybrid, asynchronous, multimodal design workshop series in a hospice in North West England. Stakeholders were introduced to core concepts in technology, design, systems thinking, and CLDs before engaging in participatory system mapping focused on the hospice experience quality. CLDs generated in workshops and through asynchronous participation were consolidated into a composite hospice system map. Twenty-seven participants, including patients, health care professionals, volunteers, managers, maintenance staff, and chaplaincy, contributed to the mapping process. The resulting map was analyzed using quantitative network analysis (in-degree, out-degree, betweenness, and closeness centrality) alongside qualitative interpretation of key system dynamics. Results: The participatory hospice system map contained 84 variables connected by 175 causal links. Network analysis highlighted patient experience (highest in-degree, 20), advanced care planning (highest out-degree, 8), fundraising (highest betweenness centrality, 0.19), and relationships with community organizations and external stakeholders (highest closeness centrality, 0.23) as central elements in the map. Qualitative analysis illuminated important dynamics, including the impact of hospital admissions and hospice stereotypes, as well as uncertainties around how advanced care planning is shaped and enacted in practice. Conclusions: Participatory system mapping with hospice stakeholders was feasible in a time-pressured setting and generated a nuanced, stakeholder-led representation of hospice system dynamics. The hybrid, multimodal workshop model enhanced access and flexibility, supporting diverse engagement. Network analysis of the CLD suggested preliminary structural and conceptual leverage points and revealed gaps in shared understanding, indicating candidate areas for service development, policy attention, and further research. Future work should examine the replicability of this approach across PEoLC settings and integrate context-specific processes to validate and act on candidate leverage points.
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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 | 0,028 | 0,030 |
| 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 | 0,007 | 0,005 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».