Children's Earliest Experiences with Death: Circumstances, Conversations, Explanations, and Parental Satisfaction
Bibliographic record
Abstract
Parents (n = 140) of children 2 to 7 years responded to an online survey regarding their children's experiences and conversations about death. A total of 75% of parents indicated that they had spoken to their child about death, and the majority of conversations were first initiated when children were between 3 and 3.5 years of age. Binary logistic regression analysis was used to explore factors that could predict conversations about death. Parents (n = 88) provided narratives of the explanations of death that they gave their child and subsequently reported their level of satisfaction with their explanation. The content of the explanations was coded and examined in relation to children's age and parental satisfaction. Results revealed that parents who provided explanations to a continued existence after death reported significantly higher levels of satisfaction than those parents who discussed the absence of a future physical relationship after death. Finally, explanations of a continued existence were not always in reference to an afterlife and could include discussing the memory of the deceased or their continued impact even after death. Thus, when talking to young children about death, parents may feel greater satisfaction in finding ways to discuss the continued legacy of those who have died compared to more biological explanations. Copyright © 2014 John Wiley & Sons, Ltd.
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 | 0.003 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".