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Enregistrement W2227965259 · doi:10.1108/he-03-2014-0034

Quantifying collaboration using Himmelman ' s strategies for working together

2015· article· en· W2227965259 sur OpenAlexaboutno aff
Megan Quinn, Jodi L Southerland, Kasie Richards, Deborah Slawson, Bruce Behringer, Rebecca Johns-Womack, Sara Louise Smith

Notice bibliographique

RevueHealth Education · 2015
Typearticle
Langueen
DomaineHealth Professions
ThématiqueSchool Health and Nursing Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGeneral partnershipThematic analysisMedical educationPsychologyHealth promotionMental healthDescriptive statisticsQuality (philosophy)Promotion (chess)Public healthNursingMedicineBusinessSociologyPolitical scienceQualitative research

Résumé

récupéré en direct d'OpenAlex

Purpose – Coordinated school health programs (CSHPs), a type of health promoting school (HPS) program adopted by Canada and the USA, were developed to provide a comprehensive approach to school health in the USA. Community partnerships are central to CSHP and HPS efforts, yet the quality of collaboration efforts is rarely assessed. The purpose of this paper is to use Himmelman’s strategies for working together to assess the types of partnerships that are being formed by CSHPs and to explore the methodological usefulness of this framework. The Himmelman methodology describes four degrees of partnering interaction: networking, coordinating, cooperating, and collaborating, with each degree of interaction signifying a different level of partnership between organizations. Design/methodology/approach – Data were collected as part of the 2008-2009 and 2009-2010 CSHP annual Requests for Proposal from all 131 public school systems in Tennessee. Thematic analysis methods were used to assess partnerships in school systems. Descriptive analyses were completed to calculate individual collaboration scores for each of the eight CSHP components (comprehensive health education, physical education/activity, nutrition services, health services, mental health services, student, family, and community involvement, healthy school environment, and health promotion of staff) during the two data collection periods. The level of collaboration was assessed based on Himmelman’s methodology, with higher scores indicating a greater degree of collaboration. Scores were averaged to obtain a mean score and individual component scores were then averaged to obtain statewide collaboration index scores (CISs) for each CSHP component. Findings – The majority of CSHPs partnering activities can be described as coordination, level two in partnering interaction. The physical activity component had the highest CISs and scored in between coordinating and cooperating (2.42), while healthy school environment had the lowest score, scoring between networking and coordinating (1.93), CISs increased from Year 1 to Year 2 for all of the CSHP components. Applying the theoretical framework of Himmelman’s methodology provided a novel way to quantify levels of collaboration among school partners. This approach offered an opportunity to use qualitative and quantitative methods to explore levels of collaboration, determine current levels of collaboration, and assess changes in levels of collaboration over the study period. Research limitations/implications – This study provides a framework for using the Himmelman methodology to quantify partnerships in a HPS program in the USA. However, the case study nature of the enquiry means that changes may have been influenced by a range of contextual factors, and quantitative analyses are solely descriptive and therefore do not provide an opportunity for statistical comparisons. Practical implications – Quantifying collaboration efforts is useful for HPS programs. Community activities that link back to the classroom are important to the success of any HPS program. Himmelman’s methodology may be useful when applied to HPSs to assess the quality of existing partnerships and guide program implementation efforts. Originality/value – This research is the first of its kind and uses a theoretical framework to quantify partnership levels in school health programs. In the future, using this methodology could provide an opportunity to develop more effective partnerships in school health programs, health education, and public health.

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: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,339
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,481
Tête enseignante GPT0,590
Écart entre enseignants0,109 · 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'étudeSans objet
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

Citations3
Publié2015
Routes d'admission1
Résumé présentoui

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