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Record W4256462689 · doi:10.1177/2167702620986096

The Futures We Want: How Goal-Directed Imagination Relates to Mental Health

2021· article· en· W4256462689 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Psychological Science · 2021
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersMarsden FundNeurological Foundation of New ZealandUniversity of Auckland
KeywordsPsychologyGoal pursuitMental healthValence (chemistry)Relevance (law)CognitionPsychological interventionPositive psychologyCognitive psychologyGoal settingDreamSocial psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Imagination is an adaptive ability that can be directed toward the pursuit of personal goals. Although there is a wealth of research on goals and on imagination, few studies lie at the intersection—little is known about individual differences in goal-directed imagination. In 153 adults, we examined how 28 aspects of goal setting, pursuit, and goal-directed imagination relate to mental health. Higher well-being and lower depressive symptoms were strongly linked (a) to having goals that were more attainable, under control, and expected to bring more joy and (b) to goal-directed imagination that was clearer, more detailed, more positive, and less negative. Importantly, the emotional valence of goal-directed imagination strongly predicted well-being at a 2-month follow-up even after controlling for mental health at baseline. These findings underscore the relevance of goal-directed imagination to well-being and depressive symptoms and highlight potential targets for goal- and imagery-based interventions to improve mental health.

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.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.117
GPT teacher head0.543
Teacher spread0.426 · 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