Check Up GP: A Co-Designed Health and Lifestyle Screening App to Improve Patient-Centered Care for Young People in Primary Care
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".