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Record W2955037191 · doi:10.26686/wgtn.17136290

Exploring the sustaining factors that motivate nurses to work in the rural areas of Papua New Guinea

2019· dissertation· en· W2955037191 on OpenAlexfundno aff
Priscilla Poga

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
FundersScience and Engineering Research BoardPrairie Oat Growers AssociationWorld Bank Group
KeywordsDisadvantagedGovernment (linguistics)New guineaWork (physics)Developing countryRural areaFace (sociological concept)NursingRural healthEconomic shortageHealth careMedicineEconomic growthPolitical sciencePsychologySociologySocial science

Abstract

fetched live from OpenAlex

Papua New Guinea is among other developing countries that are faced with a critical shortage in human resources in health, specifically nurses, and rural areas are the most affected. Initially, and perhaps unsurprisingly, there were only two relevant studies that directly related to Papua New Guinea in this area based on the literature search. The first of these two studies was focused on nurses and the social aspect of rural motivation compared to the other which was focused on rural health professionals in general. Nevertheless, other research studies were eventually found from other developing countries such as certain Pacific, African and Latin American countries that served to assist in focusing the research on the chosen topic. This descriptive-exploratory study set out to explore the sustaining factors that influenced existing nurses to remain (or otherwise) in their work in Papua New Guinea’s rural areas. As such, the study involves 10 rural nurses with over two years of rural work experience in two different organisations; government and church. The interviews were semi-structured and were designed to explore the motivating factors for rural nurses and how any challenges, or demotivating factors, were overcome. The interviews were conducted in the common spoken language Tok Pisin which was translated into English, transcribed and analysed thematically. Overall the study found that rural nurses are disadvantaged because they struggle with limited resources to deliver effective health care, and they also face several personal challenges which are often overlooked. The main findings are categorised under two major themes, 1) safety and 2) socioeconomic, and each are explored by further exploration of the themes and sub-themes that are evident in both. The implications of this study are examined, including recommendations, to develop policies that are designed to address the ongoing needs of rural nurses in Papua New Guinea.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.177
GPT teacher head0.443
Teacher spread0.266 · 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 designQualitative
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
Published2019
Admission routes1
Has abstractyes

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