Intense classification training to increase the detection of drowning swimmers

Laxton, V. ORCID: 0000-0001-5590-4398, Howard, C.J. ORCID: 0000-0002-8755-1109, Guest, D. ORCID: 0000-0003-4514-9186 and Crundall, D. ORCID: 0000-0002-6030-3631, 2022. Intense classification training to increase the detection of drowning swimmers. Applied Cognitive Psychology. ISSN 0888-4080

Full text not available from this repository.


Lifeguards engage in a continuous process of deciding whether swimmers are in danger or not. The variety of behaviours that distressed swimmers show makes it difficult to impart declarative knowledge to this effect during lifeguard training. As an alternative, we propose a novel training tool that requires novice participants to rapidly categorise 3-s video clips of real-life swimmers as either 'safe' or 'drowning'. A control group also completed a sham intervention, with surfers that may 'fall'. Due to the complex nature of swimming pools, a scaffolded training approach was employed, which gradually increased the amount of background information over subsequent training rounds. Results demonstrated that the drowning classification training improved responses in a subsequent drowning detection test, compared with the active control-group. The scaffolded approach appeared to prepare participants for processing swimmers in the drowning-detection test. The results provide a foundation for a novel training protocol to improve lifeguard skills.

Item Type: Journal article
Publication Title: Applied Cognitive Psychology
Creators: Laxton, V., Howard, C.J., Guest, D. and Crundall, D.
Publisher: Wiley
Date: 29 December 2022
ISSN: 0888-4080
Rights: © 2022 The Authors. Applied Cognitive Psychology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Divisions: Schools > School of Social Sciences
Record created by: Linda Sullivan
Date Added: 17 Jan 2023 11:45
Last Modified: 17 Jan 2023 11:45

Actions (login required)

Edit View Edit View


Views per month over past year


Downloads per month over past year