An integrative review on individual determinants of enrolment in National Health Insurance Scheme among older adults in Ghana
Bibliographic record
Abstract
BACKGROUND: We conducted an integrative review in an attempt to methodically and systematically understand the individual (personal factors) that influence National Health Insurance Scheme [NHIS] enrolment among older adults aged 50 years and above. The study was premised on evidence pointing to a state of little or no change in the enrolment rates [especially among older adults], which contrasts with the initial euphoria that greeted the launch of the scheme - which culminated in high enrolment rates. METHODS: The integrative literature review was conducted to synthesise the available evidence on individual determinants of NHIS among older adults. The methodological approach of the integrative literature review follows a five-stage interdependent and interconnected procedure of problem identification, literature search, data evaluation, data analysis and results presentation. Studies that met the inclusion criteria were peer-reviewed articles published in the English Language, from January 2010 to July 2020 and have Ghana as its setting or study area. The Andersen's Behavioural Model was used to categorize the predictor variables. RESULTS: Predisposing factors [gender, age, level of education and marital status], enabling factors [income] and need factors [health conditions or health attributes of older adults] were identified as individual predictors of NHIS enrolment among older adults. The findings support argument of Andersen's Behavioural Model [where predisposing, enabling and need factors are considered as individual determinants of health behaviour]. CONCLUSIONS: The findings call for policy reforms that take into account the aforementioned individual predictors of NHIS enrolment, especially among the aged.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".