Community readiness and momentum: identifying and including community-driven variables in a mixed-method rural palliative care service siting model
Notice bibliographique
Résumé
BACKGROUND: Health service administrators make decisions regarding how to best use limited resources to have the most significant impact. Service siting models are tools that can help in this capacity. Here we build on our own mixed-method service siting model focused on identifying rural Canadian communities most in need of and ready for palliative care service enhancement through incorporating new community-driven insights. METHODS: We conducted 40 semi-structured interviews with formal and informal palliative care providers from four purposefully selected rural communities across Canada. Communities were selected by running our siting model, which incorporated GIS methods, and then identifying locations suitable as qualitative case studies. Participants were identified using multiple recruitment methods. Interviews were transcribed verbatim and the transcripts were reviewed to identify emerging themes and were coded accordingly. Thematic analysis then ensued. RESULTS: We previously introduced the inclusion of a 'community readiness' arm in the siting model. This arm is based on five community-driven indicators of palliative care service enhancement readiness and need. The findings from the current analysis underscore the importance of this arm of the model. However, the data also revealed the need to subjectively assess the presence or absence of community awareness and momentum indicators. The interviews point to factors such as educational tools, volunteers, and local acknowledgement of palliative care priorities as reflecting the presence of community awareness and factors such as new employment and volunteer positions, new care spaces, and new projects and programs as reflecting momentum. The diversity of factors found to illustrate these indicators between our pilot study and current national study demonstrate the need for those using our service siting model to look for contextually-relevant signs of their presence. CONCLUSION: Although the science behind siting model development is established, few researchers have developed such models in an open way (e.g., documenting every stage of model development, engaging with community members). This mixed-method study has addressed this notable knowledge gap. While we have focused on rural palliative care in Canada, the process by which we have developed and refined our siting model is transferrable and can be applied to address other siting problems.
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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 | 0,060 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,005 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».