MétaCan
Menu
Back to cohort
Record W4388790945 · doi:10.22605/rrh8294

Exploring the ideas of young healthcare professionals from selected countries regarding rural proofing

2023· article· en· W4388790945 on OpenAlexafffund
Ian Couper, Manoko Innocentia Lediga, Ndivhuho Beauty Takalani, Mayara Floss, Alexandra E Yeoh, Alexandra Ferrara, Amber Wheatley, Lara Feasby, Marcela Araújo de Oliveira Santana, Mercy Wanjala, Sneha P Kotian, Veronika Rasic, Vuthlarhi Shirindza, Alan Bruce Chater, Theadora Swift Koller

Bibliographic record

VenueRural and Remote Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsIsland Health
FundersUniversiteit StellenboschGovernment of CanadaWorld Health Organization
KeywordsFocus groupThematic analysisHealth careContext (archaeology)Qualitative researchRural areaNursingPublic relationsRural healthMedicineMedical educationPolitical sciencePsychologySociologyGeographySocial science

Abstract

fetched live from OpenAlex

INTRODUCTION: Globally, most countries struggle to meet the health needs of rural communities. This has resulted in rural areas performing poorly when compared to urban areas in terms of a range of health indicators. There have been few coherent or systematic strategies that target rural communities and address their needs within the rural context. Rural proofing, defined as the systematic application of a rural lens across policies and guidelines to ensure that they speak to these health needs, seeks to address this gap. The healthcare professionals (HCPs) who will be called upon to advocate for and lead the implementation of rural proofing efforts are those currently in training or early career stages. We thus sought to understand the perspectives of young HCPs regarding the concept of rural proofing. METHODS: The study adopted an interpretivist paradigm. Data were collected using semi-structured individual interviews and focus group discussions (FGDs). Selected HCPs who are in leadership in Rural Seeds, a movement for young HCPs, participated in the study. FGDs in the form of Rural Cafés were led by some Rural Seeds leaders who participated in the interviews and who showed interest in organising the discussions. Eleven exploratory interviews and six FGDs were conducted using Zoom. HCPs were from Australia, Europe, Africa, North America, South America, and Asia. Interviews and FGDs were conducted in English, recorded, and transcribed verbatim. Thematic analysis was then undertaken. RESULTS: Participants perceived the state of rural healthcare globally to be problematic. Access to care was seen as the most significant issue in rural health care, associated with the challenges of lack of equity in access, and limited funding and support for healthcare professionals and their career pathways. Despite varying understanding of the concept, rural proofing was seen to be of great value in improving rural health care. A number of ideas for applying rural proofing, with examples, were proposed from their perspectives as frontline healthcare providers. They particularly recognised the importance of addressing the local needs of rural communities and the needs of present and future HCPs. Implementation of rural proofing was seen to require the involvement of key stakeholders from a range of sectors at multiple levels. CONCLUSION: Given the state of rural health, young rural HCPs suggest that rural proofing strategies are needed as they have the potential to bring about equity in the delivery of health care in rural and remote communities. These strategies will assist in creating a more positive future for rural health care worldwide and motivate young HCPs to become involved in rural health care, as well as to increase their motivation to take an interest in health policy development. These strategies need to be applied at multiple levels, from national government to local contexts. It is also seen to be critically important to involve multiple levels of stakeholders, from politicians to healthcare providers and community members, in the process of rural proofing.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.408
Teacher spread0.328 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueRural and Remote HealthSame topicGlobal Health Workforce IssuesFrench-language works237,207