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Record W7117309885 · doi:10.1017/ipm.2025.10153

Global mental health-building capacity and developing links to support postgraduate training in psychiatry in Zambia: Zambia-Ireland Psychiatry Academic Collaboration

2025· article· en· W7117309885 on OpenAlexaff
Anne M. Doherty, Francisca Bwayla, Paul Ravi, Fiona McNicholas, Fiona Fenton, John D. Sheehan

Bibliographic record

VenueIrish Journal of Psychological Medicine · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsGeneral partnershipMental healthChild and adolescent psychiatryMental health serviceService (business)Training (meteorology)Population

Abstract

fetched live from OpenAlex

This paper outlines the experience of establishing a partnership for clinical services and academic training across two very different countries: Zambia and Ireland. Drawing upon the experience of setting up other similar international partnerships, the process of understanding the two very different cultural and clinical contexts has developed collaboratively over the course of the past 2 years, and has resulted in valuable insights and joint working. In line with local priorities, the Zambian colleagues in the partnership have prioritised the support for postgraduate training in psychiatry to enable the expansion of services, and allow greater population access. In particular, there is evidence of unmet need in the areas of substance use psychiatry, and child and adolescent psychiatry.The collaboration will increase cultural awareness among psychiatrists and trainees in Ireland and deepen their knowledge and understanding of international psychiatry. This programme is mutually beneficial and has the potential to greatly improve relationships between the mental health service providers of the two nations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.476
Teacher spread0.380 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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