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Record W7055201286

CASE 5: A Stakeholder Analysis: Developing an Indigenous-Specific Intercultural Competency Training Module (Part A)

2021· article· en· W7055201286 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2021
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsStakeholderStakeholder engagementBrainstormingIndigenousStakeholder analysisHealth careCultural competenceQuality (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

The case focuses on developing an Indigenous-specific intercultural competency training module and outlines the steps needed to achieve this, with particular emphasis on the importance of conducting a stakeholder analysis and developing a stakeholder engagement plan. The protagonist of the case, Nia Singh, heads up the Intercultural Education Program at the Southwestern Ontario Intercultural Education Centre. After working at the organization as an intercultural education specialist for several years, Nia is looking to expand her project portfolio by redesigning the Intercultural Education Program’s pre-existing Indigenous intercultural competency training module. Nia determines the objective of the new training module will be to educate health care workers about the importance of intercultural competency within health care organizations. Specifically, the module will focus on Indigenous populations and will aim to improve the quality of care they receive so their long-term health outcomes ultimately improve. Nia works with her colleague, Steven Miller, to complete a stakeholder analysis and engagement plan, and they use four different steps to accomplish this: 1) brainstorming all possible stakeholders who have a vested interest in the training module; 2) prioritizing and categorizing each stakeholder as a core stakeholder, involved stakeholder, supportive stakeholder, or peripheral stakeholder; 3) determining the level of engagement required for each stakeholder; and 4) determining which engagement strategies to use for each stakeholder. After completing the stakeholder analysis and engagement plan, Nia and Steven arrange to interview the key stakeholders in order to gather additional opinions, ideas, and perspectives related to developing the training module. These stakeholders include health care workers, Indigenous community members, and other relevant informants. Once the interview process is complete, Nia and Steven develop a pilot version of the training module that is ready to be implemented on a small scale. However, Nia and Steven know they still have their work cut out for them in terms of identifying an effective implementation strategy. This case is intended to be a skills practice case with the primary objective of having students learn about conducting a stakeholder analysis and then learn about stakeholder engagement. By examining this case and completing the learning team activity, students will be able to understand the importance of stakeholder analysis and stakeholder engagement as they relate to developing an Indigenous-specific intercultural competency training module. Once students have acquired this knowledge, they will be able to apply stakeholder analyses and engagement strategies to developing a variety of public health programs. However, given that the training module focuses on Indigenous populations, the case will focus on concepts related to health equity and the barriers faced by Indigenous people when they access health care services. A secondary learning objective is for students to acquire knowledge pertaining to intercultural competency, particularly in terms of its significance within the field of public health and how it can be used as a strategy for reducing health disparities for other marginalized populations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.207
GPT teacher head0.301
Teacher spread0.094 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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