Why is ECE enrollment so complicated? An analysis of barriers and co-created solutions from the frontlines
Bibliographic record
Abstract
• We engaged both parents and staff to understand barriers to ECE enrollment. • We used peer researchers and human-centered design methods. • We identified novel barriers related to outmoded technology. • Peer researchers cocreated eight novel policy/ system solutions for ECE enrollment. Numerous studies have examined the processes parents use in accessing early care and education (ECE) for their children and the barriers parents face to enroll. To our knowledge, previous studies have not engaged both parents and frontline ECE enrollment staff as co-investigators to examine family perspectives and a systems perspective simultaneously. This qualitative study compiled a research team of diverse (family, provider, academic) perspectives combining principles of community-based participatory research (CBPR) and human-centered design in peer-led qualitative interviews ( n = 20), focus groups ( n = 5) with local ECE staff and managers, and several community synthesis and design sessions ( n = 6) with caregivers, ECE staff, and local thought leaders in Cincinnati, Ohio. The goals of the study were to: 1) identify policy-relevant and system-level barriers that keep families with low incomes or families of color from enrolling in high-quality ECE programs and 2) co-design potential policy- and system-interventions or prototypes with parents and local ECE agency partners to overcome these barriers. Nine types of barriers in three categories were elucidated by parents and ECE staff: 1) enrollment barriers such as parents’ lack of awareness of options, excessive and redundant paperwork, outdated technologies used, and lack of transparency paired with poor follow-up communication from ECE staff; 2) practical and logistical barriers such as cost, transportation, and concerns about COVID; and 3) human-factors concerns related to safety, trust, and diversity of ECE environment. Peer researchers co-created eight policy- or system- prototypes or interventions to address these barriers. While our findings suggest that access challenges remain ubiquitous locally, they also demonstrate what is possible when researchers and policymakers intentionally involve targeted users of ECE policy in the designs of those policies and systems.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".