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Record W2971946482 · doi:10.1007/s11013-019-09641-w

Mental Health System Reform in Contexts of Humanitarian Emergencies: Toward a Theory of “Practice-Based Evidence”

2019· article· en· W2971946482 on OpenAlexfundno aff
Hanna Kienzler

Bibliographic record

VenueCulture Medicine and Psychiatry · 2019
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchCouncil for British Research in the LevantWellcome TrustMcGill University
KeywordsMental healthPublic relationsEvidence-based practicePublic healthPolitical scienceSociologyEthnographyPublic administrationPsychologyMedicineNursingPsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

Humanitarian emergencies such as armed conflicts are increasingly perceived as opportunities to improve mental health systems in fragile states. Research has been conducted into what building blocks are required to reform mental health systems in states emerging from wars and into the barriers to reform. What is less well known is what work and activities are actually performed when mental health systems in war-affected resource-poor countries are reformed. Questions that remain unanswered are: What is it that international humanitarian aid workers and local experts do on the ground? What are the actual activities they perform in order to enable and sustain system reform? This article begins to answer these questions through ethnographic case studies of mental health system reform in Kosovo and Palestine. Based on the findings, a theory of "practice-based evidence" is developed. Practice-based evidence assumes that knowledge is derived from practice, rather than the other way around where practice is believed to be informed by systematic evidence. It is argued that a focus on practice rather than evidence can improving system reform processes as well as the provision of mental health care in a way that is sensitive to local contexts, structural realities, culture, and history.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.588

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)0.0010.000

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 head0.038
GPT teacher head0.363
Teacher spread0.325 · 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 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

Citations27
Published2019
Admission routes1
Has abstractyes

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