Determining the Requirements of Vulnerable Groups for Health Counseling and Optimizing the Evaluation of Health Consultations: Mixed Methods Study With the Use of AI
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".