Developing a Business Continuity Plan for the Canadian Memorial Chiropractic College
Bibliographic record
Abstract
The purpose of this research is to develop a robust Business Continuity Plan (BCP) for the Canadian Memorial Chiropractic College (CMCC, or “the College”) by answering the primary research question of what are the critical processes, functions, services and products that must be delivered by each department at CMCC. This report describes the methods and methodology used to develop the BCP and a series of recommendations for the successful implementation at the College. \n \nThe CMCC recently established an Enterprise Risk Management (ERM) Program at the College, which is responsible for assessing and planning for all risks that the College may face. Business Continuity Planning is a type of risk management and is therefore housed within the ERM Program. The goal of BCP is to identify and plan for risks associated with uncontrollable external events that may jeopardize the College’s ability to deliver essential services and products. Uncontrollable external events that would fall under Business Continuity include both natural and man-made disasters; for example, extreme weather events, fires, earthquakes, information technology failures, and terrorist attacks. \n \nA Business Impact Analysis was conducted for each department at CMCC that would go on to inform the development of a BCP Template. The data for the Business Impact Analysis (BIA) was gathered using a Business Impact Survey designed specifically for CMCC department heads. The survey was distributed to 15 key CMCC staff along with an opt-out consent form and 13 surveys were returned and included in the Business Impact Analysis. \n \nAfter the completion of the Business Impact Analysis, a BCP Template was designed, which includes all the necessary components. to be filled out by department BCP Leads. \n \nThe main findings of the research were the critical processes, functions, products and services for each department at CMCC, as well as the maximum acceptable timeframes for resuming these critical operations after a disruption has occurred. These critical operations were specific to each department, therefore justifying the need for individual departmental BCPs. \n \nTo develop effective BCP at CMCC, it is essential that the departmental BCPs are robust and accurately reflect the critical operations of each department. A well-defined BCP Policy and clearly defined roles with respect to BCP activity will help to ensure that CMCC is well prepared to withstand the negative effects of an uncontrollable external event and to avoid significant harm to the College.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".