Becoming the best mom that I can: women's experiences of managing depression during pregnancy – a qualitative study
Bibliographic record
Abstract
BACKGROUND: The purpose of this constructivist grounded theory study was to develop a theoretical model that explains women's processes of managing diagnosed depression when pregnant. METHODS: We explored the experiences of 19 women in Ontario who were diagnosed with depression during their pregnancy. RESULTS: The model that emerged from the analysis was becoming the best mom that I can. Becoming the best mom that I can explains the complex process of the women's journey as they travel from the depths of despair, where the depression is perceived to threaten their pregnancy and their ability to care for the coming baby, to arrive at knowing the self and being in a better place. In order to reground the self and regain control of their lives, the women had to recognize the problem, overcome shame and embarrassment, identify an understanding healthcare provider, and consider the consequences of the depression and its management. When confronting and confining the threat of depression, the women employed strategies of overcoming barriers, gaining knowledge, and taking control. As a result of counseling, medication, or a combination of both, women felt that they had arrived at a better place. CONCLUSION: For many women, the idea that depression could occur during pregnancy was antithetical to their vision of the pregnant self. The challenge for a pregnant woman who is diagnosed with depression, is that effective care for her may jeopardize her baby's future health. This provides a dilemma for about-to-be parents and their healthcare providers. Improved awareness of depression during pregnancy on the part of healthcare professionals is needed to improve the women's understanding of this disorder and their ability to recognize and seek help with depression should it occur during the prenatal period. Further qualitative research is needed to determine the specific aspects that need to be addressed in such classes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".