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Record W2115583188 · doi:10.1093/heapol/15.2.194

Conceptualizing and applying a minimum basic needs approach in southern Philippines

2000· article· en· W2115583188 on OpenAlexaffabout
Tuula Heinonen

Bibliographic record

VenueHealth Policy and Planning · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGovernment (linguistics)BusinessResource (disambiguation)Public relationsEnvironmental resource managementEconomic growthEnvironmental planningKnowledge managementPolitical scienceGeographyComputer scienceEconomics

Abstract

fetched live from OpenAlex

This study, a collaboration between Canadian and Filipino researchers, focuses on how the national government's Minimum Basic Needs (MBN) Approach has been implemented at the local level in some selected sites in Region XI on the Philippine island of Mindanao. This case study of MBN implementation focuses on the experiences of three municipalities and three barangays (villages) within them. The research explores, through interviews and group discussions, what the mayors, technical working groups and volunteer health workers in these areas thought about MBN and how they participated in the initiative. The objectives of the study were: to explore models of MBN data utilization at the municipal and barangay levels; to understand how the MBN data guided decision-making about community priorities and resource allocation; to examine the role that community volunteers played in promoting the use of MBN data, and in community health and development activities which ensued; and to determine what factors challenged or encouraged the use of MBN data for social development at the barangay level. In all the sites, MBN had some impact, most often due to methods of concentrating information on unmet basic needs locally and making use of it in planning and project development processes. The findings show that although there is still some way to go before MBN is effectively integrated into local planning and project development, some responses to problems have been implemented and innovative projects were undertaken or being considered.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.339
Teacher spread0.271 · 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 designNot applicable
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

Citations9
Published2000
Admission routes2
Has abstractyes

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