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Record W2583593084 · doi:10.26443/ijwpc.v4i1.123

Who experiences depressive symptoms following mindfulness-based stress reduction and why?

2017· article· en· W2583593084 on OpenAlexaffvenue
Patricia L. Dobkin, Kaveh Monshat

Bibliographic record

VenueInternational Journal of Whole Person Care · 2017
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsToronto Rehabilitation InstituteMcGill University
Fundersnot available
KeywordsMindfulnessMindfulness-based stress reductionCoping (psychology)Stress reductionClinical psychologyDepressive symptomsPsychologyDistractionDepression (economics)MeditationMedicinePsychotherapistPsychiatryAnxiety

Abstract

fetched live from OpenAlex

Background: A small percentage of patients screen positive for depression following a mindfulness-based program. We identified patient characteristics associated with this outcome in order to understand this phenomenon.Methods: Depressive symptoms, stress, mindfulness, coping with illness and sense of coherence were measured in 126 patients with various medical and psychological conditions pre- and post- Mindfulness-Based Stress Reduction (MBSR). Results: Fewer patients (27% vs. 49%) screened positive for depression post-MBSR. Both pre- and post-MBSR patients who were depressive following MBSR scored lower on meaningfulness, comprehensibility, and manageability (sense of coherence), higher on emotional coping and lower on palliative and distraction coping. Smaller positive changes (e.g. stress) occurred in these patients as well. Viewing life as less meaningful pre-MBSR predicted more symptoms of depression post-MBSR.Conclusions: Patients who suffered depressive symptoms following the program were unable to reappraise their lives in such a way as to become stress resilient.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.350
Teacher spread0.324 · 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 designQualitative
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

Citations6
Published2017
Admission routes2
Has abstractyes

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