The Impact of Avalanche Education on Risk Perception, Confidence, and Decision-Making among Backcountry Skiers

Figures & data

Figure 1. Logic model for avalanche education.

Figure 2. Example photo for survey risk perception: scenario 2.

Table 1. Reasons for enrolling in avalanche fundamentals (N = 184).

Gain confidence in avalanche terrain	78.80
Learn about terrain management	75.54
Improve decision-making skills	76.63
Learn more about the snowpack	72.83
Owed it to partners to be a stronger member of my ski/riding group	69.57
To meet like-minded people or backcountry partners	21.20
Had experience; felt like it was time for a formal course	9.78
My parents wanted me to prior to backcountry skiing/riding	9.73
Other	6.52

Note: Percentages do not add to 100 because participants were instructed to “select all that apply.”

Table 2. What individuals learned from avalanche fundamentals.

Category	N/%	Description	Example quotations
Terrain assessment	92 (60.14%)	Slope angle/measurement, route management, slope aspect, terrain features, terrain based risk recognition.	Slope Angle plays a big role in avalanche terrain. Traveling safe and away from avalanche terrain will keep you away from avalanches.
Snowpack assessment and forecast	126 (70.63%)	Digging snowpack test pits, Snowpack compression tests (ECT), understanding the avalanche forecast, making snowpack observations.	If a slope looks unstable, take some tests, and if in doubt don’t ski on it. How to dig a pit and do an extended column test to look for instability and propagation.
Rescue skills	105 (63.64%)	Using avalanche safety gear (beacon/shovel/probe), victim search patterns, emergency procedures.	How long I have to save a friend. How to better use rescue equipment.
Human factor/decision-making	59 (32.17%)	Decision-making skills, group communication, partner considerations, trip planning, habit and value acquisition.	How to make decisions on how/if to proceed in the backcountry. The importance of communication with group members.
Weather	6 (4.20%)	Weather indicators, assessment, and impact	How weather influences avalanche danger.
Other skills/ terminology/general statements	28 (18.88%)	Avalanche terminology, general statements, skills or knowledge not captured in other categories.	“Bullseye” data Definitions of avalanche terms

Notes: N is the frequency with which a category was mentioned across all responses and participants who answered the question. % indicates the percentage of respondents who described learning an item within the category. Participants could mention multiple items in a single category. Percentages do not add to 100 because participants reported three things they learned.

Table 3. Multi-level linear regression models predicting risk perception, perception confidence, and willingness to ski.

	Risk perception	Confidence	Would ski
	b (se)	b (se)	b (se)
Fixed effects
Time	−0.37 (0.09)***	1.55 (0.04)***	−0.28 (0.18)
Scenario type
Uncertain	0.54 (0.08)***	−0.15 (0.05)***	−0.38 (0.17)*
Risky	1.55 (0.09)***	−0.10 (0.05)*	−1.62 (0.21)***
Time X scenario type
X uncertain	0.82 (0.12)***
X risky	0.51 (0.13)***
Female	0.08 (0.09)	−0.16 (0.14)	−0.77 (0.27)**
Experienced	−0.15 (0.11)	0.72 (0.16)**	0.71 (0.31)*
Risk attitudes	0.02 (0.03)	0.11 (0.04)**	0.19 (0.08)*
Risk perception			−2.04 (0.10)***
Perception confidence			0.21 (0.07)**
Constant	3.24 (0.22)***	3.12 (0.31)***	8.62 (0.75)***
Random effects
Constant variance	0.27 (0.04)	0.73 (0.08)	2.14 (0.38)
Error variance	1.45 (0.04)	0.97 (0.03)
Nindividuals	184	184	184
Nobservations	2465	2515	2426

Notes: N_observations is the combined total number of photos scored for all of the participants in each model. The residual variance for multi-level logistic regression is π²/3 or 3.29. *p<.05, **p<.01, ***p<.001

Table 4. Multi-level linear regression models predicting perception confidence.

	Gender X time b (se)	Experience X time b (se)	Risk attitudes X time b (se)	Engagement X time b (se)
Fixed effects
Time	1.35 (0.05)***	1.82 (0.04)***	2.09 (0.11)***	−3.24 (.59)***
Female	−0.38 (0.14)*	−0.08 (0.14)	−0.16 (0.14)	−0.16 (0.14)
Experienced	0.72 (0.16)**	1.30 (0.16)***	0.72 (0.16)***	0.73 (0.16)***
Risk attitudes	0.11 (0.04)**	0.11 (0.04)**	0.17 (0.04)***	0.11 (0.04)**
Time X female	0.43 (0.08)*
Time X experienced		−1.16 (0.09)***
Time X risk attitudes			−0.12 (0.02)***
Time X engagement				0.71 (0.09)***
Engagement				−0.30 (0.15)*
Constant	3.06 (0.21)***	2.98 (0.31)***	2.85 (0.31)***	5.09 (1.05)***
Random effects
Individual variance	0.73 (0.08)	0.74 (0.08)	0.73 (0.08)	0.72 (0.08)
Error variance	0.96 (0.03)	0.91 (0.03)	0.96 (0.03)	0.95 (0.03)
Nindividuals	184	184	184	184
Nobservations	2515	2515	2515	2515

Notes: N_observations is the combined total number of photos scored for all of the participants in each model. Models also include scenario type. *p<.05, **p<.01, ***p<.001