A Bibliometric Review of Counselors’ Professional Competency Literature
Bibliographic record
Abstract
Research on counselor professional competencies has gained attention due to its crucial role in shaping effective counseling practices and strengthening professional identity. Although a growing body of literature exists, the motivations, contexts, and findings remain diverse. Therefore, this study conducts a bibliometric analysis to map the patterns and directions of research on counselor professional competencies. A total of 241 documents, comprising 223 journal articles and 18 reviews, were extracted from the Scopus database covering the period from 1957 to 2025. We employed RStudio, VOSviewer, and Microsoft Excel for citation, content, and network analyses. The bibliometric analysis was complemented by a systematic review of relevant publications. The results reveal that the United States dominates the research output, followed by Australia, Canada, and the United Kingdom. Co-authorship patterns show relatively low levels of international collaboration. The cartography analysis identified three major research clusters: (1) ethics and professional practice in genetic counseling, (2) clinical supervision and counselor professional development, and (3) clinical competencies, counseling practice, and therapeutic effectiveness. This study represents the first bibliometric review of counselor professional competencies, addressing gaps in the literature and offering directions for future research, particularly in the integration of supervision, cultural competence, and the development of professional identity in diverse socio-cultural contexts.
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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 | 0.015 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.272 | 0.309 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".