Assessment Tools for Children who Experience Traumatic Loss: A Systematic Review
Bibliographic record
Abstract
Children who experience the traumatic (i.e., violent and/or unexpected) death of a loved one are at risk for a range of adverse developmental and mental health problems, including pathological processes of grief. Over the last decades, conceptualizations of maladaptive grief have varied, resulting in a range of assessment tools and no “gold standard” measure to assess symptoms of prolonged grief in children. The current paper is a systematic review of studies that measured grief in children who experienced traumatic loss in order to determine the measures currently used in the literature with children who experience traumatic loss. Searches were conducted according to the preferred reporting items for systematic reviews and meta-analyses in PUBMED, PsycINFO, and OVID and through hand searches of relevant reference lists. Two authors reviewed each study yielded by searches and conducted data extraction on included studies. Studies were included if they were peer-reviewed, included a measure of grief, and consisted of samples of children (age 18 and younger) whereby at least a portion experienced traumatic loss. Thirty-nine studies met inclusion criteria, from which 17 measures were identified. The most commonly used measure was the Inventory of Complicated Grief ( n = 10 studies) followed by the Extended Grief Inventory ( n = 6). Most studies used different measures and variations of the same measures to assess similar constructs. All but one measure relied on child self-report. More standardization of measurement across studies is needed, along with parent and/or teacher reported measures.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".