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Record W2140250947

Experimental Test of Possible Psychological Benefits of Past-Life Regression

2004· article· en· W2140250947 on OpenAlexafffund
Kellye Woods, Imants Barušs

Bibliographic record

VenueScholarship@Western (Western University) · 2004
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsWestern University
FundersKing's University College
KeywordsPsychologyTest (biology)ConsciousnessRegression analysisSession (web analytics)Post hocSocial psychologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Abstract—The purpose of this study was to determine whether past-life regression can lead to increased psychological well-being and changes in fundamental beliefs about consciousness and reality among those who are psychologically healthy. Twenty-four undergraduate students each participated in a single guided imagery session in which they were given either a past-life or open suggestion. Participants who were given the past-life suggestion had better scores on some measures of psychological well-being than those given the open suggestion, although post-hoc tests did not reveal any differences in psychological well-being or beliefs between those who actually experienced past-life imagery and those who did not. There was an overall shift for all participants toward more transcendent beliefs as a result of their involvement in the guided imagery sessions. The roles in past-life regression of depth of altered experience and belief in past lives were also examined.

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)0.001
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.129
GPT teacher head0.383
Teacher spread0.254 · 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

Citations3
Published2004
Admission routes2
Has abstractyes

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