Internet Method in Bereavement Research: Comparison of Online and Offline Surveys
Bibliographic record
Abstract
This study examines the reliability and validity of Internet research in bereavement. Recent literature demonstrates an increased interest in utilizing a more convenient, inexpensive, and rapid Internet method to collect data and recruit bereaved participants. For researchers, the Internet offers the possibility to learn more about grief from the growing online community of bereaved people. To explore the possible use of Internet tools in bereavement research, this study compares online survey method with traditional paper-and-pencil method in grief assessment. One group of bereaved adults (N = 84) was recruited and completed the survey by mail, and another group of bereaved adults (N = 262) was recruited and completed the same set of questions via Internet. The collected data were analyzed to identify both similarities and differences between the two samples' responses and the psychometric characteristics of the Core Bereavement Items inventory (CBI; Burnett, Middleton, Raphael, & Martinek, 1997). Significant differences were found between the two samples in relation to time since loss, cause of death, and relationship to deceased, demonstrating a greater variability in the Internet sample. Other demographic characteristics, as well as the grief and meaning making scores, did not differ significantly. In addition, no relevant differences were found in the psychometric properties of the CBI. These findings suggest that the Internet-based methods can be a suitable and valid alternative to more traditional paper-and-pencil methods.
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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 | 0.037 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".