An Internet Resource for Self-Assessment of Mental Health and Health Behavior: Development and Implementation of the Self-Assessment Kiosk
Bibliographic record
Abstract
BACKGROUND: Standardized measurement of physical and mental health is useful for identification of health problems. Personalized feedback of the results can influence health behavior, and treatment outcomes can be improved by monitoring feedback over time. However, few resources are available that are free for users, provide feedback from validated measurement instruments, and measure a wide range of health domains. OBJECTIVE: This study aimed to develop an internet self-assessment resource that fills the identified gap and collects data to generate and test hypotheses about health, to test its feasibility, and to describe the characteristics of its users. METHODS: The Self-Assessment Kiosk was built using validated health measurement instruments and implemented on a commercial internet survey platform. Data regarding usage and the characteristics of users were collected over 54 weeks. The rate of accrual of new users, popularity of measurement domains, frequency with which multiple domains were selected for measurement, and characteristics of users who chose particular questionnaires were assessed. RESULTS: Of the 1435 visits, 441 (30.73%) were visiting for the first time, completed at least 1 measure, indicated that their responses were truthful, and consented to research. Growth in the number of users over time was approximately linear. Users were skewed toward old age and higher income and education. Most (53.9%, 234/434) reported at least 1 medical condition. The median number of questionnaires completed was 5. Internal reliability of most measures was good (Cronbach alpha>.70), with lower reliability for some subscales of coping (self-distraction alpha=.35, venting alpha=.50, acceptance alpha=.51) and personality (agreeableness alpha=.46, openness alpha=.45). The popular questionnaires measured depression (61.0%, 269/441), anxiety (60.5%, 267/441), attachment insecurity (54.2%, 239/441), and coping (46.0%, 203/441). Demographic characteristics somewhat influenced choice of instruments, accounting for <9% of the variance in this choice. Mean depression and anxiety scores were intermediate between previously studied populations with and without mental illness. Modeling to estimate the sample size required to study relationships between variables suggested that the accrual of users required to study the relationship between 3 variables was 2 to 3 times greater than that required to study a single variable. CONCLUSIONS: The value of the Self-Assessment Kiosk to users and the feasibility of providing this resource are supported by the steady accumulation of new users over time. The Self-Assessment Kiosk database can be interrogated to understand the relationships between health variables. Users who select particular instruments tend to have scores that are higher than those found in the general population, indicating that instruments are more likely to be selected when they are salient. Self-selection bias limits generalizability and needs to be taken into account when using the Self-Assessment Kiosk database for research. Ethical issues that were considered in developing and implementing the Self-Assessment Kiosk are discussed.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".