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Record W2327918660 · doi:10.7748/ns.29.22.44.e9250

Community mental health initiatives in Pakistan

2015· article· en· W2327918660 on OpenAlexaff
Gulnar Ali, Nasreen Lalani, Nadia Ali Muhammad Ali Charania

Bibliographic record

VenueNursing Standard · 2015
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Alberta
FundersUniversity of Huddersfield
KeywordsMental healthPsychologyPolitical scienceNursingMedicinePsychiatry

Abstract

fetched live from OpenAlex

AIM: To identify mental health needs in local communities, and provide and evaluate nurse-led services to promote community mental health in Karachi, Pakistan. METHOD: Using an action research approach, mental health nurses implemented activities to promote mental health and psychiatric rehabilitation in 15 urban communities in Karachi. The activities were planned and implemented in collaboration with a community-based social welfare organisation. FINDINGS: Community mental health interventions were implemented by a multidisciplinary team including nursing educators and postgraduate nursing students. Positive transformation of the mental health of clients in the community was found. CONCLUSION: The challenges in identifying, diagnosing and rehabilitating clients with mental health needs in the community in Pakistan is acknowledged, and the benefits of enhanced support for clients and their families provided by community mental health nurses are described.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.000

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 head0.110
GPT teacher head0.506
Teacher spread0.396 · 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 designObservational
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

Citations8
Published2015
Admission routes1
Has abstractyes

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