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Record W2565314320 · doi:10.2196/iproc.6125

Check Up GP: A Co-Designed Health and Lifestyle Screening App to Improve Patient-Centered Care for Young People in Primary Care

2016· article· en· W2565314320 on OpenAlexvenueno aff
Marianne Webb, Greg Wadley, Sylvia Kauer, Lena Sanci

Bibliographic record

VenueIproceedings · 2016
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsPrimary careAffect (linguistics)Mental healthYoung adultMedicineYoung personHealth careHealth screeningPsychologyPsychiatryGerontologyFamily medicineDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.026
GPT teacher head0.340
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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