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Record W2806260043 · doi:10.1177/1084822318779371

Case Manager Resource Allocation Decision-Making for Adult Home Care Clients: With Comparisons to a High Needs Pediatric Home Care Clients

2018· article· en· W2806260043 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHome Health Care Management & Practice · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsData collectionContext (archaeology)Nonprobability samplingResource (disambiguation)Principal (computer security)Sample (material)Resource allocationFocus groupHealth carePsychologyBusinessOperations managementNursingMedicineComputer scienceMarketingSociologyPopulationEngineeringPolitical scienceGeography

Abstract

fetched live from OpenAlex

Home care programs have become integral parts of the overall health service system in Canada and in many other developed nations. Resource allocation decision-making by home care case managers (CM) is a complex task where CMs are challenged to meet the dual responsibilities for clients, in order that they achieve high quality care, and to the system to contain costs. The purpose of this study was to extend what is known about resource allocation decision-making factors identified in a previous systematic literature review and ethnographic study within a high needs pediatric context conducted by the principal investigator in Western Canada. Spradley’s ethnoscience method was used in this research. The study sample consisted of 17 home care CMs, professional practice leads, and their managers from two separate home care offices. All participating CMs had assigned caseloads and were involved in the assessment and implementation of care planning for clients. Purposive sampling methods were employed. In keeping with Spradley’s ethnoscience approach, data collection occurred in three distinct phases or rounds. The first round of data collection began with a series of one-on-one interviews with card sorts, the second round of data collection was another series of one-on-one interviews with CMs who were not interviewed in the prior round, and the third and final round of data collection was a focus group to accomplish further refinement and verification of our established categories. Participants identified five categories of factors that effected their resource allocation decision-making. The categories were related to one of five main areas: the client, the CM, the home care program, community resources, or the health care system. The findings of this study reinforced the complexity of CM resource allocation decision-making in home care. This study provides new insights into CM resource allocation decision-making based on multidisciplinary, integrated home care teams caring for adults, the majority of whom are 65 years and older. This study also provides the comparison of taxonomy that differs between pediatric and adult home care populations that influence resource allocation decision-making.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.391
Teacher spread0.367 · 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