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Record W2586814968 · doi:10.1177/0030222817690403

Posttraumatic Growth Following the Loss of a Pet: A Cross-Cultural Comparison

2017· article· en· W2586814968 on OpenAlexaboutno aff
Cori Bussolari, Janice Habarth, Satoko Kimpara, Rachel Katz, France Carlos, Amy Y. M. Chow, Hisao Osada, Yukiko Osada, Betty J. Carmack, Nigel P. Field, Wendy Packman

Bibliographic record

VenueOMEGA - Journal of Death and Dying · 2017
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPosttraumatic growthPsychologyCross-cultural studiesCross-culturalClinical psychologyDemographySocial psychologyAnthropologySociology

Abstract

fetched live from OpenAlex

The current study examined Posttraumatic Growth (PTG) experienced by bereaved pet owners in the United States, French-Canada, Japan, and Hong Kong following the death of their pet. Using qualitative methodology, we analyzed responses of participants who answered "yes" to a question about experiencing PTG and explored to what extent the cross-cultural responses mapped onto the five factors of the Posttraumatic Growth Inventory (PTGI). For the U.S. sample, 58% of responses mapped onto the PTGI. For French-Canada, 72% of responses mapped onto the PTGI. For Japan, 50% of responses mapped onto the PTGI and for Hong Kong, 39% of responses mapped onto the PTGI. We also explored emergent categories related to PTG for individuals who have lost a pet and discerned the unique aspects for PTG across cultures.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.075
GPT teacher head0.415
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2017
Admission routes1
Has abstractyes

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