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Record W3000148408 · doi:10.22215/etd/2019-13742

Accountability in Ontario's Health Care System: The Role of Governance and Information in Managing Stakeholder Demands

2019· dissertation· en· W3000148408 on OpenAlexaffabout
Marc Pilon

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsCarleton University
Fundersnot available
KeywordsAccountabilityCorporate governanceStakeholderPublic relationsBusinessPolitical sciencePublic administrationFinance

Abstract

fetched live from OpenAlex

This research focuses on nonprofit accountability because accountability failures, such as frauds and scandals, impede an organisation's ability to deliver on its mission and have raised concerns about an organisation's ability to manage their accountability demands.Previous nonprofit studies have focused on what accountability is and to whom organisations are accountable, while less focus has been given to how accountability is managed.Through the concepts of stakeholder relationships, governance mechanisms and information strategies, an accountability system is proposed and serves as the conceptual framework to understand how nonprofit accountability is managed.The objective of this research is to gain a better understanding of the nonprofit accountability system.As such, the study's research question is how do nonprofit organisations manage their accountability system?To answer the research question, a multiple-case study research strategy using a cross-sectional sample of health care organisations, with a particular focus on nonprofit hospitals and two of its salient stakeholders, Local Health Integration Networks (LHINs) and foundations.This study has provided a clearer understanding of the relationship between accountability, governance and information, which contrasts with a majority of studies that have concentrated on specific aspects of accountability.At the practical level, to iii view accountability as a system, makes it easier to identify weaknesses, which could be used as a governance tool to help nonprofit leaders improve their accountability management practices.Table 1-1 -Synthesis of Research Design Research theme Nonprofit accountability, governance and informationSource of the problem Practical problems: Accountability failures have raised concerns about the ability of nonprofits to manage their accountability demands and impede an organisation's ability to deliver on its mission.Accountability management is further complicated by resource constraints and a competitive external environment.Theoretical problems: Studies within the nonprofit sector have focused on what accountability is and to whom it should be given, not on what an accountability system might contain and how accountability is managed. Managerial problem How can nonprofit leaders improve accountability management practices?Research objective To gain a better understanding of the nonprofit accountability system. General research question How do nonprofit organisations manage their accountability system?Specific research questions How do nonprofit organisations use stakeholder relationships, governance mechanisms and information strategies to manage their accountability system?How do nonprofit organisations manage their stakeholder relationships?How do nonprofit organisations manage their governance mechanisms?How do nonprofit organisations manage their information strategies?How do nonprofit organisations manage their governance mechanisms?How do nonprofit organisations manage their information strategies?Type of Research Inductive and exploratory Epistemology Constructivism Research Approach Qualitative Research Strategy Case Study Data Collection Methods Interviews, Archival Documents Organisation and Participant Selection Health Care Sector Nonprofit organisations; Board members, Executives and Managers

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)0.004
metaresearch head score (Gemma)0.009
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 score0.108
Threshold uncertainty score0.787

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.007
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.275
Teacher spread0.260 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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