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Record W4321484152 · doi:10.1123/wspaj.2022-0057

Examining the Role of Physical Activity on Psychological Well-Being and Mental Health Postpartum

2023· article· en· W4321484152 on OpenAlexaff
Iris Lesser, Stéphanie Turgeon, Carl Nienhuis, Corliss Bean

Bibliographic record

VenueWomen in Sport and Physical Activity Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsBrock UniversityUniversité du Québec en OutaouaisUniversity of the Fraser Valley
Fundersnot available
KeywordsMental healthSelf-compassionAnxietyPsychologyClinical psychologyPhysical activityPostpartum depressionPsychological well-beingDepression (economics)FeelingMedicinePsychiatryPregnancyMindfulnessPhysical therapySocial psychology

Abstract

fetched live from OpenAlex

Postpartum physical activity can positively impact mental and physical health. There is a need to better understand how physical activity is related to various psychological constructs to support physical activity in postpartum women. Thus, the purpose of this exploratory, quantitative, study was to examine differences between postpartum women who were physically active and those who were physically inactive on psychological (e.g., self-compassion) and mental health constructs. Five hundred twenty-five women (Mage = 28.4) completed an online survey. Participants who reported being active following the birth of their last child had significantly higher exercise self-efficacy, self-compassion, and basic psychological needs fulfillment for exercise and significantly lower levels of perceived fatigue, anxiety, and depression compared with their inactive counterparts. However, active mothers had lower body satisfaction than inactive mothers. Women who are active after the birth of a child have improved psychological constructs that may benefit overall well-being and mental health during this challenging transition.

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)0.004
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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 integrity0.0000.000
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 head0.024
GPT teacher head0.336
Teacher spread0.312 · 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

Citations14
Published2023
Admission routes1
Has abstractyes

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