Check Up GP: A Co-Designed Health and Lifestyle Screening App to Improve Patient-Centered Care for Young People in Primary Care
Notice bibliographique
Résumé
Background: During adolescence and young adulthood, a wide range of mental health disorders and risky behaviors can emerge and co-occur. Primary care practitioners (PCPs) are ideally positioned to identify areas of concern as part of young people’s routine health care, and screening tools can help to surface problems the young person may be facing. When administered via technology, screening tools are more readily adopted by young people and can deliver results immediately to the PCP. Technology-based screening tools help PCPs to normalize sensitive issues, guide discussion about risky behavior, and promote healthy lifestyles, and they make young people more likely to disclose sensitive health issues. Despite these advantages, there is a paucity of research about how using these screening tools may affect the patient-doctor relationship and whether young people would like to use such tools again in the future. Objective: The aim was to investigate how using a health and lifestyle screening app would affect young people’s relationship with their PCP, their perception of the care they received, and whether they would like to use the app regularly. Methods: A health and lifestyle screening app (Check Up GP) for young people aged 14 to 25 was developed through a series of participatory design workshops with users and stakeholders. The app was implemented within an action research program with 4 PCPs in one primary care clinic in Melbourne, Australia. We first collected baseline data on young people’s experience of attending the clinic without using the app. Then, in the intervention phase, young people were sent a link to the app via their smartphone at the time of their appointment and asked to complete the screening prior to their appointment. The PCPs reviewed a summary report of issues immediately before seeing the young person, along with tips on youth-friendly practice and suggested actions to take on areas of concern. Results: Compared with those in the baseline group, young people using Check Up GP were significantly more comfortable asking questions and reported that their doctor knew them well, advised them how to prevent problems in the future, and was interested in the effect of the problem on their everyday lives. Check Up GP was also highly acceptable to young people, with 91% thinking it was a “good idea,” 74% reporting they would like to use the app at least once a year, and a further 21% reporting they would like to use it every time they saw their PCP. Conclusions: The results show that integrating a health and lifestyle screening app into face-to-face regular care can significantly improve and enrich young people’s experience of seeing a PCP. Using this technology has the potential to ensure typically unrecognized and preventable health and lifestyle issues in young people are not only uncovered but appropriately addressed through targeted health promotion and early intervention.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».