An Empirical Investigation of Factors Affecting Perceived Quality and Well-Being of Children Using an Online Child Helpline
Bibliographic record
Abstract
Child helplines provide free, accessible, and confidential support for children suffering from issues such as violence and abuse. Helplines lack the barriers often associated with the use of many other health services; and for many children, the helpline is the first point of contact with any kind of child protection and an important venue to go to in times of socio-economic distress. For instance, more children attempt to call the helpline in times of high unemployment, and relatively more of those conversations are about violence. Empirical evidence is scarce regarding how to implement online chat communication to improve quality and the child's well-being. In this study, we focus on the impact of chat duration, number of words, and the type of support. The results show that for children seeking emotional support, a longer chat negatively influences the immediate well-being and the counsellor needs to listen (i.e., not type), as relatively more child words result in higher evaluations. We conclude that for emotional support, the counsellor should be prepared to listen carefully, but also manage the duration. However, for children chatting for instrumental support, the counsellor needs to type more to create positive perceptions of quality. Since the impact of chat share is different for children seeking emotional support (negative) versus instrumental support (positive), counsellors need to be sensitive to early indicators of the reason for the chat.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".