Conceptualization and measurement of integrated human service networks for evaluation
Bibliographic record
Abstract
INTRODUCTION: Integration has been advanced as a strategy for the delivery of a number of human services that have traditionally been delivered by autonomous agencies with independent processes and funding sources. However, measurement of the dimensions of integration has been hampered by numerous factors, including a lack of definitional and conceptual clarity of integration, and the use of measurement tools with atheoretical foundations and limited psychometric testing. THEORY/METHODS: Based on a review of integration measurement approaches, a comprehensive approach to the measure of multiple dimensions of integrated human service networks was conceptualized. The combination of concepts was derived from existing theoretical, policy, and measurement approaches in order to establish the content validity and comprehensiveness of the proposed measure. RESULTS: The dimensions of human service integration measures are: (1) Observed (current) and expected structural inputs, or the mix of agencies that comprise the network (e.g. extent, scope, depth, congruence within an agency, and reciprocity between agencies). (2) Functioning of the network both in terms of the quality of the network or partnership functioning and ingredients of the integration of the networks' working arrangements and range of human services provided. (3) Network outputs in terms of network capacity (e.g. what is accomplished, for how many and how quickly given the local demand) measured from dual perspectives of the agency and the family. CONCLUSION: This newly developed measure unites multiple perspectives in a comprehensive approach to the measurement of integration of human service networks. Content validity has been established. Future work should focus on further refinement of this instrument through psychometric evaluation (e.g. construct validity) in diverse networks and relating these measures of network integration to client and system outcomes.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".