Home visits by paraprofessionals did not improve maternal and child health
Bibliographic record
Abstract
Olds DL, Robinson J, O’Brien R, et al. Home visiting by paraprofessionals and by nurses: a randomized, controlled trial. Pediatrics2002 ; 110 : 486 –96 [OpenUrl][1][Abstract/FREE Full Text][2] QUESTION: Do home visits by paraprofessionals (lay visitors/peer support or community workers) trained in a programme model that is effective when delivered by nurses, improve maternal and child health? Randomised (allocation concealed), blinded (data collectors), controlled trial with follow up to 24 months postpartum. 21 antepartum clinics in Denver, Colorado, USA. 735 pregnant women (mean age 20 y) who had no previous live births and either qualified for Medicaid or had no health insurance. Follow up was >80% for maternal interviews at 6, 12, 21, and 24 months postpartum; and 83% for child assessment at 21 months of age. Participants were allocated to prenatal and postpartum (≤24 mo) home visitation by paraprofessionals (PHV group, n=245) or professional nurses (NHV group, n=235), or … [1]: {openurl}?query=rft.jtitle%253DPediatrics%26rft.stitle%253DPediatrics%26rft.aulast%253DOlds%26rft.auinit1%253DD.%2BL.%26rft.volume%253D110%26rft.issue%253D3%26rft.spage%253D486%26rft.epage%253D496%26rft.atitle%253DHome%2BVisiting%2Bby%2BParaprofessionals%2Band%2Bby%2BNurses%253A%2BA%2BRandomized%252C%2BControlled%2BTrial%26rft_id%253Dinfo%253Adoi%252F10.1542%252Fpeds.110.3.486%26rft_id%253Dinfo%253Apmid%252F12205249%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=pediatrics&resid=110/3/486&atom=%2Febnurs%2F6%2F1%2F9.atom
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".