Conceptualizing and validating the human services integration measure
Bibliographic record
Abstract
PURPOSES: This paper proposes both a model and a measure of human service integration through strategic alliances with autonomous services as one way to achieve comprehensive health and social services for target populations. THEORY: Diverse theories of integrated service delivery and collaboration were combined reflecting integration along a continuum of care within a service sector, across service sectors and between public, not-for-profit and private sectors of financing services. METHODS: A measure of human service integration is proposed and tested. The measure identifies the scope and depth of integration for each sector and service that make up a total service network. It captures in quantitative terms both intra and inter sectoral service integration. RESULTS: Results are provided using the Human Service Measure in two networks of services involved in promoting Healthy Babies and Healthy Children known to have more and less integration. CONCLUSIONS: The instrument demonstrated discriminate validity with scores correctly distinguishing the two networks. The instrument does not correlate (r=0.13) with Weiss (2001) measure of partnership synergy confirming that it measures a distinct component of integration. DISCUSSION: We recommend the combined use of the proposed measure and the Weiss (2001) measure to more completely capture the scope and depth of integration efforts as well as the nature of the functioning of a service program or network.
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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 | 0.033 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".