Prevalence and correlates of depression in early childhood home visitors serving rural Colombian families
Bibliographic record
Abstract
Abstract Purpose Limited information is available on early childhood provider depression, particularly in lower and middle-income (LMIC) countries, yet evidence from diverse fields indicates that depression negatively affects work functioning. Given extensive investment worldwide in early childhood home visiting programs, understanding home visitor mental health may help improve services for families. The current investigation examined the prevalence and correlates of depression in early childhood home visitors working in rural Colombia.Methods Three hundred and forty-one home visitors (N = 341) completed the Spanish versions of the Center for Epidemiological Studies Depression Scale (CES-D) and the Knowledge of Infant Development Inventory, and self-reported socio-demographic and job-related information. Cross-sectional, clustered statistical analyses were employed in STATA Software.Results Thirteen percent of home visitors met the cut off score for depression. Higher home visitor depression was related to maternal depression among beneficiaries. Additionally, depression was higher among home visitors who were older and those who identified their marital status as separated. Depression was lower among home visitors who completed more home visits and those with higher educational attainment.Conclusions Early childhood providers experienced interconnections in their depression with those whom they served. In addition, social environmental factors related to home visitor depression were identified. The results from this study speak to the importance of considering providers’ mental health as part of the effort of disseminating high quality early childhood home visiting programs. Program and clinical implications are further discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".