Using the CollaboraKTion framework to report on primary care practice recruitment and data collection: costs and successes in a cross-sectional practice-based survey in British Columbia, Ontario, and Nova Scotia, Canada
Notice bibliographique
Résumé
BACKGROUND: Across Canada and internationally we have poor infrastructure to regularly collect survey data from primary care practices to supplement data from chart audits and physician billings. The purpose of this work is to: 1) examine the variable costs for carrying out primary care practice-based surveys and 2) share lessons learned about the level of engagement required for recruitment of practices in primary care. METHODS: This work was part of a larger study, TRANSFORMATION that collected data from three provincial study sites in Canada. We report here on practice-based engagement. Surveys were administered to providers, organizational practice leads, and up to 20 patients from each participating provider. We used the CollaboraKTion framework to report on our recruitment and engagement strategies for the survey work. Data were derived from qualitative sources, including study team meeting minutes, memos/notes from survey administrators regarding their interactions with practice staff, and patients and stakeholder meeting minutes. Quantitative data were derived from spreadsheets tracking numbers for participant eligibility, responses, and completions and from time and cost tracking for patient survey administration. RESULTS: A total of 87 practices participated in the study (n = 22 in BC; n = 26 in ON; n = 39 in NS). The first three of five CollaboraKTion activities, Contacting and Connecting, Deepening Understandings, and Adapting and Applying the Knowledge Base, and their associated processes were most pertinent to our recruitment and data collection. Practice participation rates were low but similar, averaging 36% across study sites, and completion rates were high (99%). Patient completion rates were similarly high (99%), though participation rates in BC were substantially lower than the other sites. Recruitment and data collection costs varied with the cost per practice ranging from $1503 to $1792. CONCLUSIONS: A comprehensive data collection system in primary care is possible to achieve with partnerships that balance researcher, clinical, and policy maker contexts. Engaging practices as valued community members and independent business owners requires significant time, and financial and human resources. An integrated knowledge translation and exchange approach provides a foundation for continued dialogue, exchange of ideas, use of the information produced, and recognises recruitment as part of an ongoing cycle.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».