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Record W2508975878 · doi:10.1186/s12960-016-0150-7

Impact of MPH programs: contributing to health system strengthening in low- and middle-income countries?

2016· article· en· W2508975878 on OpenAlexfundno aff
Prisca Zwanikken, Lucy Alexander, Albert Scherpbier

Bibliographic record

VenueHuman Resources for Health · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersNewfoundland and Labrador
KeywordsWorkforcePublic healthContext (archaeology)Health promotionHealth administrationHealth services researchFocus groupPromotion (chess)Public relationsMedicineNursingMedical educationPsychologyPolitical scienceBusinessMarketingGeography

Abstract

fetched live from OpenAlex

BACKGROUND: The "health workforce" crisis has led to an increased interest in health professional education, including MPH programs. Recently, it was questioned whether training of mid- to higher level cadres in public health prepared graduates with competencies to strengthen health systems in low- and middle-income countries. Measuring educational impact has been notoriously difficult; therefore, innovative methods for measuring the outcome and impact of MPH programs were sought. Impact was conceptualized as "impact on workplace" and "impact on society," which entailed studying how these competencies were enacted and to what effect within the context of the graduates' workplaces, as well as on societal health. METHODS: This is part of a larger six-country mixed method study; in this paper, the focus is on the qualitative findings of two English language programs, one a distance MPH program offered from South Africa, the other a residential program in the Netherlands. Both offer MPH training to students from a diversity of countries. In-depth interviews were conducted with 10 graduates (per program), working in low- and middle-income health systems, their peers, and their supervisors. RESULTS: Impact on the workplace was reported as considerable by graduates and peers as well as supervisors and included changes in management and leadership: promotion to a leadership position as well as expanded or revitalized management roles were reported by many participants. The development of leadership capacity was highly valued amongst many graduates, and this capacity was cited by a number of supervisors and peers. Wider impact in the workplace took the form of introducing workplace innovations such as setting up an AIDS and addiction research center and research involvement; teaching and training, advocacy, and community engagement were other ways in which graduates' influence reached a wider target grouping. Beyond the workplace, an intersectoral approach, national reach through policy advisory roles to Ministries of Health, policy development, and capacity building, was reported. Work conditions and context influenced conduciveness for innovation and the extent to which graduates were able to have effect. Self-selection of graduates and their role in selecting peers and supervisors may have resulted in some bias, some graduates could not be traced, and social acceptability bias may have influenced findings. CONCLUSIONS: There was considerable impact at many levels; graduates were perceived to be able to contribute significantly to their workplaces and often had influence at the national level. Much of the impact described was in line with public health educational aims. The qualitative method study revealed more in-depth understanding of graduates' impact as well as their career pathways.

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)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.022
GPT teacher head0.337
Teacher spread0.316 · 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.

Study designObservational
DomainEvaluation
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

Citations26
Published2016
Admission routes1
Has abstractyes

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