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Record W2188851501 · doi:10.82308/38011

Mindfulness-based medical practice: a mixed-methods investigation of an adapted mindfulness-based stress reduction program for health care professionals

2011· article· en· W2188851501 on OpenAlexfundno aff
Julie Irving

Bibliographic record

VenueeScholarship@McGill (McGill) · 2011
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsMindfulnessMindfulness-based stress reductionStress reductionPsychologyPsychotherapistStress (linguistics)Medical practiceApplied psychologyNursingClinical psychologyMedicineMedical education

Abstract

fetched live from OpenAlex

The necessity of providing health care professionals with tangible tools to manage stress and safeguard their own well-being has become increasingly apparent. Rates of burnout in the helping professions are rising; the consequences of clinician distress for patient care have been highlighted in recent literature. Past research has examined the potential for Mindfulness-Based Stress Reduction (MBSR) to act as an adjunctive intervention for various clinical problems, including but not limited to chronic pain, for over 25 years. Presently, the applications of this program are burgeoning with health care professionals, for whom the intervention holds the promise of promoting both personal and clinical benefits. Past research has examined outcomes of MBSR in health care professionals such as burnout, empathy, perceived stress, as well as medical and psychological symptoms such as depression or anxiety. The current program of research sought to expand on existing research by examining positive psychological outcomes such as well-being, as well as potential mechanisms of change such as mindful attention, and self-compassion. Quantitative and qualitative methods were employed to provide a broad portrait of how change is experienced by health care professionals engaged in the program. This dissertation comprises three manuscripts that collectively contribute to the literature. The first manuscript provides a focused literature review, summarizing the empirical literature on MBSR for health care professionals specifically. The second manuscript utilized self-report measures to explore benefits of engaging in Mindfulness-Based Medical Practice (MBMP), an adapted version of MBSR for health care professionals which includes training in mindful communication. The program was completed by a sample of 51 physicians, psychologists, social workers, nurses, and other health care professionals in two cohorts during the spring of 2008 and 2009. Findings provide initial evidence of the effectiveness of MBMP as demonstrated by significant decreases in perceived stress, and increases in mindful attention and awareness and self-compassion. Bootstrapped hierarchical regression analyses failed to reveal a moderating effect of either mindfulness or self-compassion on the negative relationship between perceived stress and well-being. The third manuscript presents a study which investigated participants' experiences of the 8-week course through focus group interviews (n = 27). A grounded theory analysis yielded a model highlighting unique change processes for practicing health care professionals in relation to enhanced awareness of perfectionism, self-criticism, and orientation to others. Participants described achieving personal outcomes such as changes in self-care attitudes and practices, as well as implications for clinical encounters with patients. This study provides one of the first in-depth qualitative investigations of practicing health care professionals' experiences of an MBSR program. Taken together, the three manuscripts provide a solid rationale for future research on the potential for MBSR to enrich the lives of health care professionals and the patients they serve.

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)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.051
GPT teacher head0.404
Teacher spread0.354 · 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 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

Citations4
Published2011
Admission routes1
Has abstractyes

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