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Record W4253617523 · doi:10.22215/etd/2014-10224

Longing for the Non-Addicted Self: Self-Discontinuity Increases Readiness to Change via Nostalgia

2014· dissertation· en· W4253617523 on OpenAlexaff
Hyoun K. Kim

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicNostalgia and Consumer Behavior
Canadian institutionsCarleton University
Fundersnot available
KeywordsGeneralizability theoryPsychologyDiscontinuity (linguistics)AddictionAction (physics)Social psychologySelfSample (material)Developmental psychologyClinical psychologyMathematicsPsychiatry

Abstract

fetched live from OpenAlex

Objective: Across three studies, self-discontinuity (i.e., a sense that the present self is different from the past self) was examined as a motivating factor for readiness to change.Moreover, nostalgia was assessed as the mediating variable in this relationship.Method: Self-discontinuity was both measured (Study 1) and manipulated (Studies 2 and 3) among a sample of disordered gamblers (Studies 1 and 2) and problem drinkers (Study 3).In all three studies, nostalgia and readiness to change was assessed.Results: As predicted, high levels of self-discontinuity resulted in greater readiness to change to the extent that disordered gamblers felt nostalgic for the pre-addicted self (Studies 1 and 2).Study 3 extended the generalizability of the results by replicating these findings with a sample of problem drinkers.Conclusion: Highlighting the difference between people's past non addicted and present addicted selves may be an important catalyst in moving people from addiction to action.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.327
Teacher spread0.307 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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