Psychometric Properties of a Chatbot Version of the PHQ-9 With Adults and Older Adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The Patient Health Questionnaire-9 (PHQ-9) is a brief depression measure that has been validated. A chatbot version of the PHQ-9 would allow the assessment of depressive symptoms remotely, at a large scale and low cost. Objective: The current study aims to: Assess the feasibility of administering the PHQ-9 in a sample of adults and older adults via chatbot, report the psychometric properties of and identify the relationship between demographic variables and PHQ-9 total scores. Methods: A sample of 3,902 adults and older adults in the US and Canada were recruited through Facebook from August 2019 to February 2020 to complete the PHQ-9 using a chatbot. Results: A total of 3,895 (99.82%) completed the PHQ-9 successfully. The internal consistency of the PHQ-9 was 0.896 ( p < 0.05). A one factor structure was found to have good model fit [ X 2 (27, N = 1,948) = 365.396, p < 0.001; RMSEA = 0.080 (90% CI: 0.073, 0.088); CFI and TLI were 0.925 and 0.900, respectively, and SRMR was 0.039]. All of the demographic characteristics in this study were found to significantly predict PHQ-9 total score, however; their effect was negligible to weak. Conclusions: There was a large sample of adults and older adults were open to completing assessments via chatbot including those over 75. The psychometric properties of the chatbot version of the PHQ-9 provide initial support to the utilization of this assessment method.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it