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Record W1916996731 · doi:10.33151/ajp.12.5.238

Integrating a Community Paramedicine Program with Local Health, Aged Care and Social Services: An Observational Ethnographic Study

2015· article· en· W1916996731 on OpenAlexaffabout
Peter O’Meara, Michel Ruest, Angela Martin

Bibliographic record

VenueAustralasian Journal of Paramedicine · 2015
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsObservational studyCitizen journalismPublic relationsEthnographyPublic healthNursingParticipatory action researchHealth careSocial workSociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

We used an observational, ethnographic research approach to identify the nature of the relationship between public engagement and the successful integration of a community paramedicine program with local health, aged care and social services in rural Ontario. Data were collected through a combination of direct observations of practice, informal discussions, interviews and focus groups. We found evidence of public engagement during the planning and implementation stages of the program, with strong participatory processes evident. There was some evidence of a culture of inclusiveness, despite the strength of the command and control heritage in emergency health services. The community paramedicine model is well placed to facilitate greater integration between paramedic services and health, aged and social services. Public engagement incorporating both participation and inclusiveness can lead to a closer alignment and integration between paramedic services and other services. This ‘grass-roots’ approach to interacting with local communities has the potential to better integrate paramedic services as part of a less-fragmented system across the health, aged care and social service sectors.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.130
GPT teacher head0.410
Teacher spread0.280 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations19
Published2015
Admission routes2
Has abstractyes

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