Analysis of Publications Related to Social Work Education Using Bibliometric Analysis Method
Bibliographic record
Abstract
This study aims to examine academic publications on social work (SW) education between 1970 and 2024 using the bibliometric analysis method, revealing trends in the academic literature and the impact of different themes on SW education. A total of 920 academic publications on SW education, published between January 1, 1970, and May 18, 2024, were analyzed through the Web of Science (WOS) database. The publications were evaluated using the “global collaboration network,” “thematic map,” “word cloud technique,” “Lotka’s Law,” and “three-field plot” techniques, and the most studied themes, countries, journals, authors, collaborations, and keywords were analyzed. One of the most significant findings of the study is the dominant role of the United States (US) in the field of SW education. The US is followed by the United Kingdom, Canada, and Australia. It was found that the US has the highest level of international collaboration in SW education, followed by the UK and Germany. According to thematic mapping analysis, themes such as “MSW students,” “self-identification,” and “values” were the most frequently studied topics in SW education. Less frequently studied but more in-depth topics included “access,” “barriers,” and “self-efficacy”. Regarding keywords, “students” emerged as the most prominent. An analysis based on Lotka’s Law found that approximately 87% of authors contributed to the literature with only a single article. In this bibliometric analysis of publications on SW education in WOS from 1970 to 2024, a more student-centered approach is evident. Additionally, themes such as health, social justice, and cultural competence play a critical role in the literature. The US contributes the most to SW education literature, followed by countries with Anglo-Saxon traditions and social welfare systems. Regions such as Asia, Africa, and the Middle East are underrepresented in the literature, with limited academic contributions.
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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 | Observational | 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.043 | 0.491 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.039 | 0.001 |
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.
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".