Nurse home visits did not differ from standard care for prevention of recurrent child abuse
Bibliographic record
Abstract
MacMillan HL, Thomas BH, Jamieson E, et al . Effectiveness of home visitation by public-health nurses in prevention of the recurrence of child physical abuse and neglect: a randomised controlled trial. Lancet 2005;365:1786–93.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q In families referred to child protection agencies (CPAs), does a programme of home visiting by public health nurses (PHNs) plus standard care prevent recurrence of child physical abuse or neglect more than standard care alone? ### ![Graphic][5]</img>Design: randomised controlled trial. ### ![Graphic][6]</img>Allocation: concealed. ### ![Graphic][7]</img>Blinding: blinded (data collectors and outcome assessors). ### ![Graphic][8]</img>Follow up period: 2 years of intervention followed up by 1 year of observation. ### ![Graphic][9]</img>Setting: 2 CPAs in Hamilton, Ontario, Canada. ### ![Graphic][10]</img>Participants: 163 English speaking families (mean age of respondent 29 y, 95% women; mean age of index child 5 y) who had been referred to the CPA for an episode of child physical abuse or neglect that occurred in the past 3 months, and the index child was <13 years of age and still living with … [1]: {openurl}?query=rft.jtitle%253DLancet%26rft.stitle%253DLancet%26rft.aulast%253DMacMillan%26rft.auinit1%253DH.%2BL.%26rft.volume%253D365%26rft.issue%253D9473%26rft.spage%253D1786%26rft.epage%253D1793%26rft.atitle%253DEffectiveness%2Bof%2Bhome%2Bvisitation%2Bby%2Bpublic-health%2Bnurses%2Bin%2Bprevention%2Bof%2Bthe%2Brecurrence%2Bof%2Bchild%2Bphysical%2Babuse%2Band%2Bneglect%253A%2Ba%2Brandomised%2Bcontrolled%2Btrial.%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS0140-6736%252805%252966388-X%26rft_id%253Dinfo%253Apmid%252F15910951%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/external-ref?access_num=10.1016/S0140-6736(05)66388-X&link_type=DOI [3]: /lookup/external-ref?access_num=15910951&link_type=MED&atom=%2Febnurs%2F9%2F1%2F13.atom [4]: /lookup/external-ref?access_num=000229291600023&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif [9]: /embed/inline-graphic-5.gif [10]: /embed/inline-graphic-6.gif
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".