Determining the Requirements of Vulnerable Groups for Health Counseling and Optimizing the Evaluation of Health Consultations: Mixed Methods Study With the Use of AI
Notice bibliographique
Résumé
Background: Evaluating health counseling services is crucial for ensuring their quality and effectiveness. However, this process is hampered by challenges such as language barriers and limited awareness of their needs and concerns. Objective: The studies aimed to enhance and digitize an existing paper-and-pencil evaluation form for a health counseling service while gaining insights into client needs and barriers. This effort intends to adapt a health care facility's offerings to better meet client demands and implement a multilingual format for greater accessibility. Methods: The research team designed and conducted an in-depth interview study with clients of a health counseling service to gather new information. The insights regarding client demands, wishes, and social needs were used to revise and supplement the existing 1-page questionnaire (originally in German) for evaluating counseling sessions. Using artificial intelligence, the team transformed the new 3-page questionnaire into easy language with a Kunin smiley scale, translated it into 7 other languages, and created audio recordings for all questions in each language. The questionnaire was then programmed into an web-based tool, allowing data collection both on-site with tablets and through integration into the counseling service's website. This digital format is now continuously used to adapt the counseling service to clients' needs. Results: A total of 18 clients participated in the in-depth interviews, which were conducted in their native languages whenever possible and lasted between 8 and 30 minutes. The results indicated that many clients attending the counseling center are burdened by physical and mental health issues, with a significant portion of the assistance provided focused on helping clients complete various forms required by health insurance providers and medical professionals. Despite these challenges, clients expressed a high level of satisfaction with the health counseling services they received. The revised and supplemented web-based questionnaire has been completed by 41 clients. Evaluation results revealed that only 21 respondents (51%) filled out the questionnaire in the national language (German), while English and Arabic were the next most common choices, each used by 6 clients (15%). Findings regarding health burdens and the need for assistance were reaffirmed, highlighting that clients' self-perception regarding their ability for self-help is notably low. Conclusions: Contrary to previous assumptions, it was found that client interests predominantly lie in receiving help with the excessive demands imposed by institutional forms and requirements rather than solely addressing health issues. Clients showed strong satisfaction with the advice received and emphasized the necessity for multilingual health counseling services and evaluations. There is a distinct need for support in completing forms for doctors and health insurance applications. In addition, many clients expressed a lack of confidence in managing health care processes independently in the future, underscoring the need for greater awareness of available resources and support networks.
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 | 0,025 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».