SOCIAL WORKERS’ KNOWLEDGE AND PREPAREDNESS IN SERVING CLIENTS WITH EXPERIENCE OF INTERGENERATIONAL TRAUMA
Bibliographic record
Abstract
This research study explored intergenerational trauma and self-assessed competency levels of social workers who serve clients experiencing possible intergenerational trauma. Intergenerational trauma is a wide-spread issue, as it affects over a quarter of children and over half of adults in the United States. Intergenerational trauma, though affecting many clients, is not often discussed, and many mental health professionals are either uninformed on this topic or not interested. This study explored social workers’ self-assessed general knowledge of intergenerational trauma, training on intergenerational trauma, and whether they have knowledge of interventions or techniques tailored to working with families and individuals with possible intergenerational trauma. This study was carried out via survey questionnaire. The data was analyzed using statistical program, SPSS. Variables including social workers’ licensure as an LCSW, personal history of intergenerational trauma, and level of experience in the field of social work were studied. It was found that social workers generally have higher self-assessed levels of competency in serving those who have experienced intergenerational trauma. The levels of preparedness appear to be more so related to levels of personal experience, as questions measuring clinical training, supervision, and role play in this subject area yielded a relative number of participants denying their experience in these areas. This is important for those within social service organizations to acknowledge that this topic deserves further exploration.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".