Tools and Resources to Prevent Childhood Obesity in Primary Care
Notice bibliographique
Résumé
Background. Tools and resources (TRs) can help to prevent obesity in children, particularly in settings that are accessible to families and well-aligned with chronic disease prevention, such as primary care. To date, little is known about the TRs that primary care providers (PCPs) currently use to prevent childhood obesity and how they can be evaluated, and if brief and novel eHealth (electronic Health) tools can be applied to help parents prevent childhood obesity when delivered in primary care. Objectives. To (i) pilot test a new method to evaluate TRs that PCPs currently use for preventing childhood obesity in primary care, and report a preliminary descriptive assessment of these TRs, and (ii) develop, refine, and pilot test a brief eHealth tool delivered in primary care to help parents prevent obesity in children. Methods. This doctoral thesis includes a mixed methods study (Study 1) and a multi-phased study (Study 2). The first study included individual semi-structured interviews with PCPs (Phase I) and evaluated currently used TRs across three assessment checklists (Phase II). Feedback was obtained from PCPs on our coding scheme and checklist data at follow-up (Phase III). The second study included the development of a parent-based digital screening, brief intervention and referral to treatment (SBIRT) (Phase I), which was subsequently refined using focus groups with parents and stakeholders (Phase II). The modified version was pilot tested using a randomized controlled trial in primary care to assess feasibility and preliminary impact (Phase III). Results. For study 1, criteria on the checklists overlapped with PCPs’ perceptions of the suitability of TRs, but did not reflect the logistical factors that impacted their use. PCPs (n=19) reported using 15 TRs, most of which scored ‘adequate’ on the three checklists. For study 2, the SBIRT was developed by our research team and industry partners based on existing models and contemporary literature on children’s lifestyle behaviors. Refinements to the SBIRT were guided by feedback from five focus groups with health care professionals (n=20), parents (n=10), and researchers (n=8); participants viewed the SBIRT as a practical, well-designed eHealth tool, but suggested improvements to specific elements, such as weight-related terms that may elicit negative reactions from parents. Lastly, the SBIRT was pilot tested with parents (n=226) in primary care. The level of recruitment (n=226/268; 84.3%) and the proportion of parents who self-selected resources (n=194/226; 85.8%) within the SBIRT supported feasibility. At one-month follow-up, a greater proportion of parents with unhealthy weight children reported discussing weight with their pediatrician compared to those with healthy weight children (2=15.4; p<0.001). Conclusions. These studies provided a unique assessment and understanding of TRs that are used to prevent childhood obesity in primary care. The mixed methods evaluation of TRs demonstrated the usefulness of combining feedback from front-line providers with objective assessment data. Our preliminary assessment of TRs that PCPs currently use in Alberta demonstrated there is room for improvement, particularly with respect to readability levels and lack of content diversity beyond nutrition and physical activity. Based on feedback from focus group participants and pilot testing of our newly-developed eHealth tool, the SBIRT was feasible in primary care and may help to nudge parents towards accessing and using TRs that can have a positive impact on children’s lifestyle behaviors.
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 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 | 0,009 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».