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Narrowing the knowledge to action gap: A mixed methods exploration of the implementation of knowledge exchange strategies

2017· dissertation· en· W2746214290 sur OpenAlexaboutno aff
Kristin M. Brown

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2017
Typedissertation
Langueen
DomaineHealth Professions
ThématiqueCommunity Health and Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAction (physics)Knowledge managementPsychologyComputer sciencePhysics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Adolescence represents a time in which many health behaviours related to chronic disease risk are formed and carried into adult life. Schools are considered key settings for adolescent health interventions; however, despite extensive research in this area, schools face challenges implementing interventions at the local level. Knowledge exchange, in which researchers and knowledge users collaborate to discuss and apply research findings, is one strategy to reduce the “knowledge to action gap” between school health research and practice. While knowledge exchange strategies are emerging in school health research, the need for evaluation has been emphasized.
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\nThis dissertation explored knowledge exchange strategies within the first phase of COMPASS (2012-2016), a longitudinal study of Ontario and Alberta secondary schools and students. Schools received annual summaries of their students’ health behaviours and a COMPASS researcher (i.e., knowledge broker) supported them in taking action to improve student health. Mixed methods were used to examine influential factors and outcomes of the COMPASS knowledge exchange strategies.
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\nA quantitative analysis of school- and student-level data from the first three years of COMPASS found that school characteristics (e.g., school size, existing health initiatives and relationships with public health units at baseline) and study-related factors (e.g., knowledge broker assigned to the school, knowledge brokering engagement level in previous year[s]) influenced schools’ participation in knowledge brokering. Knowledge brokering engagement was significantly associated with school-level changes related to healthy eating, physical activity, and tobacco programming, but changes were not evident at the aggregate student level. 
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\nQualitative interviews with researchers (n=13), school staff (n=13), and public health staff (n=4) expanded on influential factors and outcomes regarding use of COMPASS findings and knowledge brokering engagement. Knowledge users focused on factors that influenced their use of COMPASS findings more than knowledge brokering (discussing fewer facilitators than challenges). Factors identified by researchers and knowledge users aligned with those that influence implementation of school health interventions. School and public health staff used school-specific findings to inform programming and planning; knowledge exchange provided a platform for partnerships between researchers, schools, and public health units; and also resulted in outcomes for the study and researchers. Further, outcomes suggest knowledge exchange could provide a mechanism to help schools implement a health-promoting schools approach. Altogether, the mixed methods findings raise two considerations: how can we increase school engagement in knowledge exchange and how can we ensure knowledge exchange strategies reach schools that have lower capacity to implement school health initiatives?
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\nThis research makes substantive, theoretical, methodological, and practical contributions. Substantively, it provides an evaluation of knowledge translation in school health research. Theoretically, it integrates social constructionism and social ecological theory, addressing the need for theory in evaluating knowledge translation strategies. Further, a mixed methods approach was used to examine both implementation and outcomes, which has been advocated in the literature. Practice implications are discussed related to future knowledge translation strategies in school health and public health research. Lastly, areas for future research are identified.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,589
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,156
Tête enseignante GPT0,468
Écart entre enseignants0,312 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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