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Record W2265731287 · doi:10.26443/ijwpc.v3i1.110

Pilgrims together: leveraging community partnerships to enhance workplace resilience

2015· article· en· W2265731287 on OpenAlexafffundvenue
Andrea Frolic

Bibliographic record

VenueInternational Journal of Whole Person Care · 2015
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsHamilton Health Sciences
FundersMcMaster UniversityHamilton Health Sciences
KeywordsMindfulnessContemplationPsychological resilienceResilience (materials science)DilemmaHealth careCommunity resilienceEngineering ethicsPsychologyKnowledge managementPublic relationsNursingSociologyMedicinePolitical scienceComputer sciencePsychotherapistEngineering

Abstract

fetched live from OpenAlex

Many of today’s healthcare personnel find themselves in a double-bind. The question is: How to remain connected, caring and compassionate with patients, while mitigating the impact of chronic workplace stress? Mindfulness is emerging as a means for addressing this dilemma as it has the potential to both reduce workplace stress and boost employee resilience, while enhancing the patient experience. This article describes the development of a unique collaboration between local hospitals, primary care teams and a university, aimed at bringing mindfulness to life in healthcare. This is a conventional story of program development and evaluation, as well as an unconventional story of personal discovery, community-building, and organizational transformation. Each section of the paper highlights a critical success factor that we have uncovered in our journey, and poses a series of questions for contemplation. This paper aims to fill a gap in the literature by describing the key ingredients for developing and sustaining a community-wide collaboration aimed at integrating mindfulness into the healthcare system.

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 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 score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.123
GPT teacher head0.404
Teacher spread0.281 · 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

Citations2
Published2015
Admission routes3
Has abstractyes

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