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Record W3148457129 · doi:10.22215/etd/2020-14267

Investigating the Effects of Mindfulness Meditation on Motivation

2020· dissertation· en· W3148457129 on OpenAlexaff
Aidan Smyth

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsCarleton University
Fundersnot available
KeywordsMeditationMindfulnessPsychologyAnagramAnagramsGoal theorySocial psychologyTask (project management)Cognitive psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Recent research suggests that mindfulness meditation may impair motivation towards traditional laboratory tasks.The present research explored the effects of meditation on motivation towards more meaningful pursuits (i.e., personal goals) in contrast to a traditional laboratory task (i.e., anagrams).In Study 1 (n = 200), the mindfulness condition reported greater goal motivation than the podcast condition but not the filler questionnaire condition; goal self-concordance did not moderate this effect.Moreover, goal motivation increased from before to 10 minutes after meditating.In Study 2 (n = 120), the mindfulness condition reported greater goal motivation than the podcast condition; this difference remained 20 minutes later.There were no differences between conditions in anagram motivation at any time point.Furthermore, goal motivation increased from before to after meditating, whereas anagram motivation remained the same.The present research opposes the notion that meditation impairs motivation and instead suggests that meditation may enhance goal motivation.

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)0.003
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0020.000

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 head0.030
GPT teacher head0.329
Teacher spread0.299 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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