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Record W4416848589 · doi:10.29173/pandpr29578

Phenomenology of Body Awareness in Mindfulness-based Stress Reduction (MBSR)

2025· article· en· W4416848589 on OpenAlexvenueno aff
Ingeborg van den Bold, Marjolein de Boer, Jenny Slatman

Bibliographic record

VenuePhenomenology & Practice · 2025
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPhenomenology (philosophy)MindfulnessFeelingSelf-awarenessMind–body problemHuman bodyInterpretative phenomenological analysisConsciousness

Abstract

fetched live from OpenAlex

Body awareness is considered to be an important element of mindfulness-based interventions. Although studies have been done on the effects of enhanced body awareness on health and well-being, none of these studies focused on the meaning of the body and body awareness in the teaching and learning process of enhancing one’s body awareness. In this paper, we provide a phenomenology of the body in the practice of a mindfulness-based intervention. We present a participant observation study about an eight-week mindfulness-based stress reduction (MBSR) training. We analyzed, by taking a hermeneutic-phenomenological approach, what enhancing one’s body awareness entails in this practice, and how participants experienced their bodies in this process. We identified four ways in which the body (not) appears in MBSR: as intermittently present, as fragmented, while ‘feeling good’, and while ‘not feeling good’. We discussed how these body appearances can be understood through the analytic lens of Leder’s disappearance and dys-appearance, and Zeiler’s eu-appearance, and with Van Manen’s phenomenological distinctions of the body. At the end of this paper, we considered how our findings may cast new light on one of the central tenets in mindfulness practice: to be non-judgmentally aware in the present moment.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.028
GPT teacher head0.375
Teacher spread0.348 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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