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Record W2084244154 · doi:10.1177/0146167206291782

Bolstering Implementation Plans for the Long Haul: The Benefits of Simultaneously Boosting Self-Concordance or Self-Efficacy

2006· article· en· W2084244154 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

VenuePersonality and Social Psychology Bulletin · 2006
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of OttawaMcGill University
Fundersnot available
KeywordsBoosting (machine learning)AutonomyPsychologyGoal pursuitConcordanceControl (management)Goal settingSelf-efficacyApplied psychologySocial psychologyProcess managementComputer scienceMedicineBusinessPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Recent studies suggest that implementation planning exercises may not be as helpful for long-term, self-initiated goals as for short-term, assigned goals. Two studies used the personal goal paradigm to explore the impact of implementation plans on goal progress over time. Study 1 examined whether administering implementation plans in an autonomy supportive manner would facilitate goal progress relative to a neutral, control condition and a condition in which implementation plans were administered in a controlling manner. Study 2 examined whether combining implementation plans with a self-efficacy boosting exercise would facilitate goal progress relative to a neutral, control condition and a typical implementation condition. The results showed that implementation plans alone did not result in greater goal progress than a neutral condition but that the combination of implementation plans with either autonomy support or self-efficacy boosting resulted in significantly greater goal progress.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.738
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.080
GPT teacher head0.422
Teacher spread0.342 · 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