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Record W2304334413 · doi:10.1093/pch/14.10.654

The power of building systems

2009· article· en· W2304334413 on OpenAlexaboutno aff
Neal Halfon

Bibliographic record

VenuePaediatrics & Child Health · 2009
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

In Canada and the United States, approximately 4% to 5% of young children have severe disabilities. This group, added to the broader category of all children with special health care needs, accounts for 12% to 16% of children. These two groups use more than the average amount of health care resources. When we include children with behavioural, mental health and learning problems significant enough that systems are needed to evaluate and take care of them, we are talking collectively about 30% to 40% of all children. The rest are generally considered ‘good enough’ even though only a small percentage are reaching their fullest potential. When we look at our own children we don't say, “Oh, he's good enough”. But this is the way our systems have been set up. Most kids who we focus on, from a health care standpoint, are the relatively small numbers with severe disabilities and special health needs. The kids with behavioural and mental health problems fly under the radar until they attend school. We need to determine what percentage of our children are thriving, and reset our measurement systems to promote new goals and not simply to reduce vulnerabilities. We know that children who have problems usually have multiple problems. The kids with cognitive challenges usually have health or emotional problems, but our systems are not geared to deal with this. Service delivery is fragmented, with different sectors – health, education and welfare – providing programs. Each has different funding streams and different cultures. They lack coordination, have narrow programmatic criteria for eligibility, possess variable understanding of early years issues, and operate with little accountability or expectations of responsibility from local communities. When we look across the existing early childhood service systems, we see that demand is greater than the services available. Families have complex needs that are often beyond the capability of any one service provider; families have difficulty accessing services; there are large socioeconomic gradients of access; the focus is on treatment rather than prevention/early intervention; and contact is episodic. The key is to make sure that the system is based on what we know about how health develops, and that it takes advantage of our capacity to optimize health for everyone. To accomplish this, we need to transform the health system, and early childhood is a logical place to begin. Optimizing child health requires the integration of clinical and targeted prevention with universal and broader social intervention. We have to adopt curve-shifting strategies that minimize risk and maximize the protective factors. We have to align policy at all levels of government and service sectors, and bring the sectors and silos together in more cross-sectoral efforts and place-based programs that bring together child development services and programs into a more integrated system. In health care, there is much discussion about costs, quality and access; much of this discussion is centred on cost control, and in the United States, insurance reform. Reform is about making little changes that incrementally improve the existing system, whereas transformation is about upgrading to a new ‘operating system’ that drives how our health systems are organized and a new set of application programs that reflect our current knowledge and capacity. It requires a paradigm shift. To support early child development, we need to go beyond merely improving early care and education, which is mostly about service quality, performance improvement and unmet needs, to a transformative agenda that really examines how we address the health sector, the early education sector, and the family care and support sector. This requires a logic model change, and an innovation and systems change. Let's start with innovation in the health system. We have gone through two eras of health care. The first was an infectious disease era that began back in the 1700s and came to a close around the 1950s. As people began to live much longer, we entered a chronic disease era so that now, approximately 70% of all health care dollars are spent on chronic disease. Over the past century, life span has increased dramatically, from approximately 75 years to approximately 80 years. Disabilities have decreased by approximately 2% per year over the past 20 to 30 years, and mortality has decreased by approximately 1% per year. There has also been an increase in performance span, in which the IQ of the population has increased by approximately 15 points over the past century. We are now sitting at the cusp of a new era – moving away from the focus on acute and infectious disease, germ theory, clinic-based and hospital-based medical care, and insurance-based financing, with the goal of reducing deaths. As we enter this third era, the focus will be on not only treating disease but also achieving optimal health status. We will be taking a life course approach, largely involving the determinants of health, and investing in population-based prevention. Part of this new focus is about changing the operating logic. We have gone from a definition of health as the absence of disease, to the positive concept of achieving capacity. We are going from health maintenance to optimizing the health of populations; we are looking at individuals and their communities not just as a clinical treatment model. We have gone from the biomedical to the biopsychosocial, and are trying to look at things over the lifespan, rather than as episodic. We are also following a similar logic model in the early care and education area. A recent report from the National Academies Press, entitled “Children's Health, the Nation's Wealth”, redefined children's health to a developmental definition, one that defines health as the child's ability to satisfy their needs and realize their potential. It moves us from a static definition of health to a developmental one. It recognizes that health develops across the life course and that health development can be represented by trajectories. It recognizes that there are critical and sensitive periods of development, many of which occur during the first three to five years of life, that there are epigenetic factors in how genes and environment interact, and that these factors have different impacts during different periods of development. It recognizes that toxic environments and at-risk families can change trajectories for children. A child can go into a high trajectory or a low one; which trajectory depends on the number of risk factors pushing down on that trajectory and our ability, through risk-reduction strategies, to blunt those effects versus the number of protective factors that we have. This is important throughout the lifecycle, but particularly manifests at approximately 50 or 60 years of age, when individuals begin to be symptomatic. Cardiovascular and other health problems arise, and we begin to pay massively for the differential for those on a high trajectory, versus those on a low one. Children go into at-risk trajectories because of factors such as poverty, a lack of adequate health services and family discord pushing down on them. Kids on healthy trajectories typically benefit from protective factors – preschool, health services, reading to the child, appropriate discipline, and the parents' education, emotional health and literacy. This is a model that doctors can buy into, and that people working in early education and care and family support can buy into. It is important to have a common mode. If you are going to bring sectors together, you need a common way of explaining the world. There are elements in most English-speaking countries. England's SureStart initiative began in 1998, with the Departments of Health and Education working together with the Treasury with the goal of ending child poverty. The story is that when health and education argued over responsibilities, the Treasury said, “Well, it is all my money and I'm insisting that you guys are going to work together”. SureStart went from 500 children's centres in 2004 to 3000 in 2009. They are building an early education system, akin to the public education system for older children. This works for England, but building a better early childhood system is not about replicating one blueprint. We don't need to make them all look the same. It is about building bridges, meaning that depending on your starting point, you will customize your response to your place, base and population. Australia, the United States and Canada are all taking similar actions on a smaller scale. This is all happening around the same time because we have all been influenced by recent gains in brain science, allowing us to move from a model in which we wait for children to fail before we take action, to one in which we respond from the premise that all children should succeed. This represents a big change in our social contract, and has caused huge debates between conservative and progressive parties in each of the countries. This is okay; what is debated gets attention, and what gets attention, gets action.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0080.024
Scholarly communication0.0190.032
Open science0.0040.023
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0260.008

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 head0.025
GPT teacher head0.363
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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
Published2009
Admission routes1
Has abstractyes

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