Increasing a partner’s understanding of motherhood significantly reduces postnatal distress and depression in first time mothers with low self esteem
Bibliographic record
Abstract
Matthey S, Kavanagh DJ, Howie P, et al . Prevention of postnatal distress or depression: an evaluation of an intervention at preparation for parenthood classes. J Affect Disord 2004;79:113–26.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q Does increasing a partner’s understanding of motherhood affect postnatal distress and depression in first time mothers? ### ![Graphic][5] Design: Randomised controlled trial. ### ![Graphic][6] Allocation: Concealed. ### ![Graphic][7] Blinding: Single blinded. ### ![Graphic][8] Follow up period: Six weeks and six months after giving birth. ### ![Graphic][9] Setting: Public hospital, Sydney, Australia. ### ![Graphic][10] Patients: 268 couples, expecting their first child, who were in the late second or early third trimester of pregnancy. Exclusion criteria: non-English speaking or same sex couples. ### ![Graphic][11] Intervention: Participants were randomly assigned to the treatment group (empathy; 89 couples), a non-specific control group (baby play; 78 couples) or control (101 couples). All groups consisted of the usual six weekly sessions covering pregnancy, birth, breastfeeding, and a short session on postnatal depression. The empathy group included an extra session on psychosocial issues related to first time parents which aimed to increase the couples’ understanding of each other’s concerns, provide them with strategies to deal … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Baffective%2Bdisorders%26rft.stitle%253DJ%2BAffect%2BDisord%26rft.aulast%253DMatthey%26rft.auinit1%253DS.%26rft.volume%253D79%26rft.issue%253D1-3%26rft.spage%253D113%26rft.epage%253D126%26rft.atitle%253DPrevention%2Bof%2Bpostnatal%2Bdistress%2Bor%2Bdepression%253A%2Ban%2Bevaluation%2Bof%2Ban%2Bintervention%2Bat%2Bpreparation%2Bfor%2Bparenthood%2Bclasses.%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS0165-0327%252802%252900362-2%26rft_id%253Dinfo%253Apmid%252F15023486%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/S0165-0327(02)00362-2&link_type=DOI [3]: /lookup/external-ref?access_num=15023486&link_type=MED&atom=%2Febmental%2F7%2F4%2F116.atom [4]: /lookup/external-ref?access_num=000220409400012&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 [11]: /embed/inline-graphic-7.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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".