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Record W4390271808 · doi:10.29173/ijll32

Health Sector Industrial Labour Troubles in Nigeria: Implication for Leaders and Other Stakeholders

2023· article· en· W4390271808 on OpenAlexaff
Aloysius Maduforo, Shelleyann Scott, Donald E. Scott

Bibliographic record

VenueInternational Journal for Leadership in Learning · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Challenges
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPrivate sectorGovernment (linguistics)Industrial actionHealth sectorBusinessShut downPublic sectorAdministration (probate law)Economic growthEconomic policyPolitical scienceMedicineEnvironmental healthHealth servicesLawEconomicsEngineeringPopulation

Abstract

fetched live from OpenAlex

Nigeria’s health sector has been challenged with frequent industrial labour troubles, commonly called strikes. Industrial labour disputes are the existence of incompatibility of goals, interests, and values of different persons or groups in an organization. Several causes have been identified for the strikes in Nigeria. This review focuses on the various causes of industrial troubles in Nigeria, and the consequences of the industrial action on the stakeholders. Causes were identified as: poor administration, incompetent leaders, the failure of government to keep the agreement that was signed with the unions, poor funding and poor infrastructure in the health sector, and supremacy challenge between doctors and other health workers were identified amongst other factors to be the cause of strikes in the Nigerian health sector. Unfortunately, patients were the greatest affected during these strikes, especially when the public health facilities were shut down and the patients were discharged to go home or moved to other private hospitals.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.715
GPT teacher head0.517
Teacher spread0.198 · 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 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
Published2023
Admission routes1
Has abstractyes

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