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Record W4386496711 · doi:10.18666/jorel-2023-11657

Through the Eyes of She: Exploring Women’s Stewardship and Connection to Nature Using Mindfulness and Photo Elicitation in Newfoundland and Labrador Parks and Protected Areas

2023· article· en· W4386496711 on OpenAlexaffabout
Laura Bass, TA Loeffler

Bibliographic record

VenueJournal of Outdoor Recreation Education and Leadership · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMindfulnessStewardship (theology)Mental healthPsychologyPhoto elicitationEmpowermentNarrativeFeelingDisconnectionQualitative researchPsychotherapistSociologySocial psychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Urbanization contributes to a collective disconnection from nature and an increase in mental health-related illnesses. Women were the focus for this research, as they disproportionately experience anxiety, depression, phobias, and comorbidity of conditions. This qualitative study investigated the mental health benefits of practicing mindfulness in nature, and its influence on stewardship in Newfoundland and Labrador Parks and Protected areas. Feminist narrative inquiry and semi-structured interviews were used to explore ten women’s stories from parks and nature-based experiences. Drawing on the influences of attention restoration theory and mindfulness, this research used photo elicitation to explore natural features that provoked feelings of mindfulness. Barriers to participation were gender-related issues including fear, ethic of care, and financial and time constraints. Participation was facilitated by relationships, community, empowerment, and green exercise. Practicing mindfulness in natural spaces influenced feelings of deeper connection and environmental stewardship.

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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.297

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.104
GPT teacher head0.322
Teacher spread0.218 · 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 teacher head, 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

Citations4
Published2023
Admission routes2
Has abstractyes

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