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Record W2062945380 · doi:10.2190/om.60.4.b

Internet Method in Bereavement Research: Comparison of Online and Offline Surveys

2010· article· en· W2062945380 on OpenAlexaff
Katerina Tolstikova, Brian Chartier

Bibliographic record

VenueOMEGA - Journal of Death and Dying · 2010
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsThe InternetPsychologyOnline and offlineComputer scienceInternet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.228
GPT teacher head0.494
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueOMEGA - Journal of Death and DyingSame topicGrief, Bereavement, and Mental HealthFrench-language works237,207