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Record W2158923564 · doi:10.1093/her/cyl127

The costs of a community-based intervention to promote maternal health

2006· article· en· W2158923564 on OpenAlexaff
Lisa Gold, Alan Shiell, Penelope Hawe, Therese Riley, Bree Rankin, Penny R Smithers

Bibliographic record

VenueHealth Education Research · 2006
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)NursingCommunity healthPublic healthCommunity organizationBusinessCommunity developmentPublic relationsMedicineMedical educationEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

The costs of community-level interventions are rarely reported, although such insights are needed if intervention research is to be useful to practitioners seeking to understand what might be involved in replicating interventions in different contexts. We report the costs of a 2-year community-based intervention to promote the health of recent mothers in Victoria, Australia. Program of Resources, Information and Support for Mothers was an integrated programme of primary care and community-based strategies. It had health care professional training, health education and community development components as well as an emphasis on creating 'mother-friendly' environments. Costs included the programme costs [primarily the salaries of the community development officers (CDO) in the field] and also 'induced' costs that relate to the CDOs' successes in attracting additional resources to the intervention from the local community. The total cost averaged A$272,490 per rural community and A$313,900 per urban community, equivalent to A$172.40 and A$128.70 per mother, respectively. For every A$10 of public funds initially invested in the project, the CDOs were able to attract a further A$1-2 worth of local resources, predominantly in the form of volunteer time or donated services.

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)0.039
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.378
GPT teacher head0.594
Teacher spread0.216 · 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 designOther design
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
Published2006
Admission routes1
Has abstractyes

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