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Record W2506174614 · doi:10.29060/taps.2016-1-1/oa1014

Delivering on Social Accountability: Canada’s Northern Ontario School of Medicine

2016· article· en· W2506174614 on OpenAlexaboutno aff
Roger Strasser

Bibliographic record

VenueThe Asia Pacific Scholar · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityBusinessLocal communityTransparency (behavior)SustainabilityPublic relationsHealth careEnvironmental resource managementProcess managementEnvironmental planningEconomic growthPolitical scienceGeography

Abstract

fetched live from OpenAlex

 Limited availability of cultural and linguistically appropriate services (e.g.Aboriginal, Francophones), which impacts access and outcomes  Scarcity of resources (e.g.health human resources, infrastructures, technologies, etc.) and varied enablement of health professionals to work at the full scope of practice limit the capacity of the system to deliver care at an acceptable standard -although the Panel recognizes that many rural and northern practitioners practice to their full scope of practice, policy, infrastructure and other tools are needed to enable this more consistently in rural and northern areas  Inconsistent implementation of potential interprofessional models across local communities, which are considered an important element of improved access to local health care (e.g.varied levels of investment in primary care models such as Family Health Teams across local communities) Availability of transportation (emergent, inter-facility and non-urgent) in some northern, remote and rural areas is limited  Travel distance can make access to services difficult, and influences which services individuals seek. Lack of rural perspective applied in planning at the provincial or LHIN levels, and the need for increased flexibility at the local level to drive innovations related to scope of practice, funding and system integration  A recognition that the health care access challenges and needs in rural communities differ between southern Ontario and northern Ontario, and that challenges are typically accentuated in the north  The historic trend toward centralization in health system design, which limits local responsiveness and reduces access; need to create local capacity to focus on synergies across the continuum of care and sectors  Inter-sectoral and cross-jurisdictional challenges and fragmentation of the funding, management and coordination of different components of the health system (e.g.emergency medical services, public health) Limited sharing of health records and information across professionals within the system Identifying Services that Improve AccessEqually important to identifying challenges is understanding the current programs and strategies already in place to improve access to rural, remote and northern Ontario.As part of the Panel's work, an inventory of current programs that the MOHLTC funds to address access issues in rural, remote and northern communities was established, which range in the types of services funded, including: disease-focused programs, health professional recruitment and retention, interprofessional care models, local community or population specific initiatives, travel grants and technology-enabled access solutions.While a specific review of these programs was not conducted by the Panel, there was general acceptance of the contribution that these provincial commitments have toward improving access in rural, remote and northern Ontario. Stage 1 Rural and Northern Health Care Framework/PlanBased on the inputs and insights gathered through its planning process, the Panel is proposing the following Stage 1 Framework/Plan, which outlines a vision, guiding principles, planning standards and decision guides, strategies and guidelines for the MOHLTC and LHINs. Stage 1 Rural and Northern Health Care Framework/Plan Guiding Principles

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)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.789

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0260.008
Scholarly communication0.0150.005
Open science0.0020.009
Research integrity0.0130.013
Insufficient payload (model declined to judge)0.0570.005

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 head0.056
GPT teacher head0.371
Teacher spread0.314 · 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 designQualitative
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

Citations33
Published2016
Admission routes1
Has abstractyes

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