Defining performance and competency requirements for army responders to domestic flood response operations
Bibliographic record
Abstract
Purpose The a priori identification and development of army personnel competencies are necessary to enable effective and efficient responses to rapidly changing climate conditions. Accordingly, this study aims to identify the performance requirements of a military flood responder and the competencies (knowledge, skills and abilities) required to perform it. Design/methodology/approach Using an abductive approach, the authors conducted both secondary and primary research to generate a validated framework of performance criteria and competencies for army personnel responding to floods. This literature review integrated both the peer-reviewed academic literature and public sector grey literature. Using the critical incident technique, the authors then conducted semi-structured interviews with 15 members of the Canadian Armed Forces (CAF) who had previously been tasked with flood response operations. Participants were asked about the tasks required while conducting flood response operations. Interview transcripts were then content analysed to identify themes regarding those tasks, and the competencies needed to perform those tasks were then extracted and contrasted with the literature review findings. Inter-rater reliability for the analysis was established via iterative discussion between the two co-authors. Findings The primary data reinforced and expanded the list of performance expectations that the authors deductively identified from the integrated literature review, adding granularity to each. It also identified competencies (including both hard and soft skills) and highlighted previously neglected contextual antecedents of military flood response effectiveness. Research limitations/implications though knowledge saturation was achieved from the 15 interviews conducted, further research with larger samples could more deeply ground the evidence discovered in this study. Nevertheless, the competencies identified in this paper could serve as a starting guide to staffing and/or training interventions targeted at improving these competencies for personnel responding to flood scenarios. Practical implications The theoretical findings also have immediate practical relevance to training for flood response operations. In particular, the subtle challenges in competency crossover from military operations to flood response operations may facilitate not only more efficient, targeted training (that could improve the effectiveness of army personnel involved in humanitarian roles), but could be applied to the selection of army personnel as well. This study may also help provincial/municipal operators and emergency planners by better communicating the strengths and limitations of army personnel in addressing civilian military cooperation for humanitarian operations. Thus, the findings of this research study represent an important first step in prompting attention to the strategic human resource planning studies required to make all responders more efficient and effective in their respective division of labour within the humanitarian domain. Social implications Peering a little beyond these research findings, human-induced climate change is expected to continue increasing the frequency of such events (IPCC, 2021), and a timely, national force is likely to be increasingly required for Canadians impacted by major disasters stemming from natural hazards when local resources become overwhelmed. Yet, there is some concern from the CAF that increasing responsiveness to disaster operations will affect their military readiness (Leuprecht and Kasurak, 2020). One can indeed envision a paradox whereby the CAF is both a “force of last resort” while increasingly becoming a “first choice for domestic disaster and emergency assistance”. The practical implications from this research also suggest that military personnel, while fully capable of successfully conducting flood response operations, may become overburdened and less able to adopt yet greater capacity and training for other additional humanitarian work. Nevertheless, the competencies highlighted by participants can help inform the next flood response operation in Canada. Originality/value Most literature in the field of emergency response focuses on cooperation between civilian and military resources and other strategic-level themes. The findings address critical granularity missing at the operational and tactical levels of humanitarian assistance and disaster relief research. The authors also draw implications beyond the military context, including for local/regional governmental players (operators and emergency planners) as well as for volunteers in flood response roles.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".