Informal settlements and the care of children 0–3 years of age: a qualitative study
Bibliographic record
Abstract
Background: There is a rapid increase in urbanization with a high percentage of people living in poverty in urban informal settlements. These families, including single parents, are requiring accessible and affordable childcare. In Mlolongo, an informal settlement in Machakos County in Nairobi metropolitan area, Kenya, childcare centres, referred to as 'babycares' are increasing in number. They are being provided by local community members without attention to standards or quality control. The study objective was to understand parents', caregivers' and community elders' experiences and perceptions in terms of the quality of babycares in Mlolongo to inform the design and implementation of improved early childcare services. Methods: Using a community-based participatory research philosophy, a qualitative study including focus group discussions with parents, community elders and babycare centre employees/owners (referred to as caregivers) was conducted in Mlolongo. Results: A total of 13 caregivers, 13 parents of children attending babycares, and eight community elders participated in the focus groups. Overall, community elders, parents and caregivers felt that the babycares were not providing an appropriate quality of childcare. The reported issues included lack of training and resources for caregivers, miscommunication between parents and caregivers on expectations and inappropriate child to caregiver ratio. Conclusion: The deficiencies identified by respondents indicate a need for improved quality of affordable childcare to support early child development in these settings. Efforts need to be invested in defining effective models of early childcare that can meet the expectations and needs of parents and caregivers and address the major challenges in childcare quality identified in this study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".