STUDY PROTOCOL Open Access acceptability of a computer tablet-based prenatal psychosocial assessment (e-screening) compared to paper-based screening.
Bibliographic record
Abstract
Full list of author information is available at the end of the articleSecondary objectives are to compare the two modes of screening on: (1) the level of detection of prenatal depression and anxiety symptoms and psychosocial risk; (2) the level of disclosure of symptoms; (3) the factors associated with feasibility, acceptability, and disclosure; (4) the psychometric properties of the e-version of the assessment tools; and (5) cost-effectiveness. A sample of 542 women will be recruited from large, primary care maternity clinics and a high-risk antenatal unit in an urban Canadian city. Pregnant women are eligible to participate if they: (1) receive care at one of the recruitment sites; (2) are able to speak/read English; (3) are willing to be randomized to e-screening; and (4) are willing to participate in a follow-up diagnostic interview within 1 week of recruitment. Allocation is by computer-generated randomization. Women in the intervention group will complete an online psychosocial assessment on a computer tablet, while those in the control group will complete the same assessment in paper-based form. All women will complete baseline questionnaires at the time of recruitment and will participate in a diagnostic interview within 1 week of recruitment. Research assistants conducting diagnostic interviews and physicians will be blinded. A qualitative descriptive study involving healthcare providers from the recruitment sites and women will provide data on feasibility and acceptability of the intervention. We hypothesize that mental health e-screening in primary care maternity settings and high-risk antenatal units will be as or more feasible, acceptable, and capable of detecting depression, anxiety, and psychosocial risk compared to paper-based screening.
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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 | 0.031 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.114 | 0.011 |
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".