A Bibliometric Analysis of Publication Trends of Empirical Studies Over the Past 50 years Using the Child Attachment Studies Catalog and Data Exchange (CASCADE)
Bibliographic record
Abstract
This study describes the historical trends and gaps of attachment research through a bibliometric analysis of past research on observational measures of child-parent attachment. Given the substantial amount of research that has been amassed on child-parent attachment since the publication of Bowlby and Ainsworth’s seminal work in the 1970s, this study provides an important synthesis of the state of child attachment research. This analysis leverages the Child Attachment Studies Catalog and Data Exchange (CASCADE) catalog, a repository of all empirical studies using observational measures of child-parent attachment. The bibliometric analysis includes a total of 2,318 peer-reviewed articles, book chapters, and dissertations from 1970 to 2023. Key study characteristics described include parent gender, geographical location, publication growth over time, publication venue, population samples, measurement tools, primary predictors, and main outcomes. Key findings reveal that the vast majority of child-parent attachment research over the past 50 years has been conducted on child-mother dyads (88.3%), has been set in North America (72.8%) or Europe (18.9%), is drawn predominantly from community samples (61.5%), and includes 24.4% of individuals of ethnic minority status. A small number of studies are conducted on samples with a demographic or health risk (24.7%), clinical risk (8.3%), or from foster/adoptive backgrounds (4.4%). Findings from this bibliometric analysis highlight the need for more attention to non-maternal caregivers, increased research on child attachment in non-Western settings, and more studies on at-risk populations including those from low socioeconomic backgrounds.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.060 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.005 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".