Anger in the context of postnatal depression: An integrative review
Bibliographic record
Abstract
BACKGROUND: Contrary to social constructions of new motherhood as a joyous time, mothers may experience postnatal depression and anger. Although postnatal depression has been thoroughly studied, the expression of maternal anger in the context of postnatal depression is conceptually unclear. This integrative review investigated the framing of anger in the context of postnatal depression. METHODS: After undertaking a search of CINAHL, Ovid-Medline, PsycInfo, and Web of Science, we identified qualitative (n = 7) and quantitative (n = 17) papers that addressed maternal anger and postnatal depression. We analyzed the data by developing themes. RESULTS: Our review indicated that anger was a salient mood disturbance for some postnatally depressed women with themes integrated as: (i) anger accompanying depression, (ii) powerlessness as a component of depression and anger, and (iii) anger occurring as a result of expectations being violated. CONCLUSIONS: Our findings indicate that anger can coexist with women's postnatal depression. Anger can be expressed toward the self and toward children and family members with negative relationship effects. We recommend that health care providers and researchers consider anger in the context of postnatal mood disturbances.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".