Facilitators of Maternal Affective Attachment Bonds in Various Family Contexts
Bibliographic record
Abstract
The main objective of this thesis was to identify facilitators of strong maternal affective attachment bonds to children. First, a systematized review of the literature was conducted to gather and synthesize all the research over the last 25 years that has identified correlates and predictors of maternal affective attachment. The review found 26 articles relevant to the research question, and main findings from the existing literature were summarized. The main study of the thesis was built upon the findings of the review using data collected through an online survey of Canadian mothers. First, a latent profile analysis (LPA) was used to cluster mothers into maternal profiles based on their patterns of responses to measures of previously identified correlates and predictors of the maternal affective attachment; symptom distress related to symptoms of depression and anxiety, the dimensions of avoidance and anxiety in mothers’ adult romantic attachment, and mothers’ sense of parental efficacy and satisfaction in the maternal role. Then, a MANOVA was conducted to determine whether profile membership would account for a significant portion of the variance in the maternal affective attachment bond to children. Results indicated that maternal profiles characterized by lower symptom distress, lower romantic attachment avoidance and anxiety, and higher efficacy and satisfaction in the parental role reported higher affective attachment, and perceived more closeness and less conflict in their relationships with their children. The results of this thesis help to inform the scholarship of motherhood by identifying salient maternal experiences associated with positive family outcomes.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".