Predictors of Burnout Among Child Counselors
Bibliographic record
Abstract
The 2022 National Healthcare Quality and Disparities report disclosed that approximately a quarter of children ages 3–17 in the United States experience a mental health disorder. As a result, there is a growing need for well-trained child counselors to provide support services for this population. However, numerous systematic reviews have highlighted the prevalence of burnout among child counselors, which can compromise the quality of care. In response, researchers have sought to explore factors related to burnout among child counselors. While several studies have identified the risks of professional burnout, limited research focuses on burnout among child counselors. This study investigated how self-efficacy, workload, and parent disengagement predict burnout among child counselors. A multiple regression analysis indicated that 63 percent of the variance in burnout was accounted for by the predictor variables. Our results demonstrated that addressing factors such as self-efficacy, workload, and parent disengagement through targeted interventions, such as professional development, workload management strategies, and parental engagement programs; may help mitigate burnout among child counselors, thus enhancing the sustainability and effectiveness of mental health services for children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".