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Record W2742678618 · doi:10.18584/iipj.2017.8.3.1

The Indigenous Experience of Work in a Health Research Organisation: Are There Wider Inferences?

2017· article· en· W2742678618 on OpenAlexvenueno aff
Sharon Chirgwin, Adrienne Farago, Heather d'Antione, Trish Nagle

Bibliographic record

VenueInternational Indigenous Policy Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousNature versus nurtureFlexibility (engineering)AttritionGovernment (linguistics)Work (physics)Public relationsQualitative researchSociologyNursingMedical educationPsychologyPolitical scienceMedicineManagementEngineeringSocial science

Abstract

fetched live from OpenAlex

The purpose of this study was to identify the factors that positively and negatively impacted on the employment experiences and trajectories of Indigenous Australians who are currently or were formerly employed by a research organisation in both remote and urban settings. The study design was an embedded mixed-methods approach. The first phase quantified staff uptake, continued employment, and attrition. Then interviews were conducted with 42 former and 51 current Indigenous staff members to obtain qualitative data. The results showed that the quality of supervision, the work flexibility to enable employees to respond to family and community priorities, and training and other forms of career support were all identified as important factors in the workplace. The most common reasons for leaving were that research projects ended, or to pursue a career change or further study. The authors use the findings to make recommendations pertinent to policy formation for both government and organisations seeking to attract and nurture Indigenous staff.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.001
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.002
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 head0.237
GPT teacher head0.570
Teacher spread0.333 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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