MétaCan
Menu
← Back to cohort
Record W4236790146 · doi:10.32920/ryerson.14647563.v1

Assessment, Allocation, and Management of Home and Community Care Services for Medically Complex Children : A Case Study

2021· preprint· en· W4236790146 on OpenAlexaffabout
David Salib

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsToronto Metropolitan UniversityCanadian Institutes of Health Research
Fundersnot available
KeywordsEconomic shortageCase managementHealth careBusinessNursingService (business)Needs assessmentResource allocationCommunity serviceMedicinePublic relationsMarketingEconomic growthPolitical scienceManagementEconomics

Abstract

fetched live from OpenAlex

This study examines the experiences of CCAC Care Coordinators when assessing, allocating, and managing medically complex children who require home and community care services. A case-study design was implemented, employing a focus group with seven Care Coordinators and an analysis of the 14 Local Health Integration Networks (LHINs) Integrated Health Service Plans (IHSPs) across Ontario. Three major findings arose from the study. First, families are experiencing increased levels of burden related to the child's care responsibilities. Second, there remains a health human resource shortage of individuals with a specialization in paediatrics in the home and community sector. Third, Care Coordinators function as street-level bureaucrats when allocating publicly funded services without the use of a standardized assessment tool. Ultimately, a model of care needs to be implemented supporting a balanced approach to assessment, utilizing standard assessment tools while providing a means for Care Coordinators to utilize their expertise in allocating services.

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0100.004
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.096
GPT teacher head0.486
Teacher spread0.390 · 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 designCase report
Domainnot available
GenreEmpirical

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
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicPrimary Care and Health Outcomes→French-language works237,207→