Food Protection Trends - November/December 2020 - 426

combined with a person's motivation to comply, normative
beliefs determine that person's subjective norm (4). Therefore,
the indirect measures of attitudes, subjective norms, and
perceived behavioral controls include behavioral beliefs and
outcome evaluations, normative beliefs and motivation to
comply, and control beliefs and power of control, respectively.
After significant beliefs have been determined, interventions
such as educational materials and training can be developed
and/or modified specifically to elicit true behavior changes.
For example, employees may feel that proper hand washing is
not important because they had not washed their hands and,
to their knowledge, no one became ill as a result. Interventions
must target these specific beliefs by providing concrete examples, such as how foodborne outbreaks did occur because of
poor performance of the behavior, to help employees understand why these beliefs are problematic.
This study was conducted to compare beliefs about three
food safety practices-proper cleaning and sanitizing of
food contact surfaces, proper hand washing, and use of a
thermometer-between two groups of school nutrition
employees: those who absolutely intended to practice
each behavior and those who did not. The results provide
information that food safety professionals can use to develop
effective interventions that impact food safety training and
behaviors in the child nutrition environment.
MATERIALS AND METHODS
Instrument
The TPB served as the theoretical basis for the study.
A questionnaire was adapted from previous research and
enhanced by the results of an elicitation study (5, 21), which
followed the recommendations of Ajzen (3) by including
both indirect measures (behavioral beliefs, normative beliefs,
and control beliefs) and direct measures (attitudes, subjective
norms, and perceptions of control) of behavioral intention
among school nutrition employees. Ajzen noted that direct
measures often have low reliability, but by utilizing indirect
measures to evaluate the beliefs participants have about a
particular concept, researchers can more fully understand
each individual's attitudes, subjective norms, and perceived
behavioral controls. The indirect measures are combined into
a composite score, which represents the construct and reduces
the concern about reliability (3).
The university Institutional Review Board approved the
study protocol prior to data collection. For pilot testing,
the survey was mailed to a convenience sample of 101
school nutrition employees. In addition to completing the
questionnaire, participants provided feedback on clarity of the
statements and time required to complete the questionnaire,
and changes in format and content were incorporated
according to recommendations. For example, the statement
"Some of the questions may appear to be similar but they
address different issues and practices" was added to the
instructions section of the questionnaire to explain the

426

Food Protection Trends November/December

repetitiveness of some of the statements. The data collected
from the pilot test were not included in the final study.
The final questionnaire booklet included 31 questions
to measure eight constructs, including direct and indirect
measures for each behavior and respondent demographics.
For each practice, the questions were the same, but the stem
of the question changed based on the practice it was referring
to (Appendix). Each practice had a total of eight questions.
Before each section, a detailed definition of each behavior was
provided. Employee attitudes were measured with a set of five
semantic 7-point scales as recommended by Ajzen (4): goodbad, worthless-valuable, useless-useful, unpleasant-pleasant,
and foolish-wise. Subjective norms, behavioral intentions, and
perceived behavioral controls were measured with a 7-point
scale anchored from strongly disagree (1) to strongly agree (7).
Sample
The sample comprised school nutrition employees
affiliated with 163 school districts from seven randomly
selected states. Each state represented one of the seven
regions defined by the U.S. Department of Agriculture
(USDA) Food and Nutrition Service (FNS) at the time
of the study: Mid-Atlantic, Midwest, Mountain Plains,
Northeast, Southeast, Southwest, and Western (Fig. 2).
A list of school districts and operational demographics
from the National Center for Education Statistics Web
site (https://nces.ed.gov/ccd/districtsearch/) was
compiled for each state and categorized by size to ensure
representation of districts of different sizes. Districts were
classified as mega for ≥ 40,000 students, large for 20,000 to
39,999 students, medium for 2,500 to 19,999 students, and
small for < 2,500 students. A convenience sample of school
districts was then selected, and the researchers attempted
to distribute the questionnaires uniformly among the seven
states based on availability of schools and district size. A
total of 12 mega school districts, 30 large school districts,
54 medium school districts, and 67 small school districts
were included in the study.
An electronic postcard was sent to school nutrition program
directors to invite their employees to participate in the study
and to alert them to expect questionnaires via mail delivery.
The number of questionnaires sent to each school district was
determined by the research team based on the size of the district.
Each mega and large school district received 50 questionnaires,
medium school districts received 20 questionnaires, and small
school districts received 10 questionnaires. School nutrition
directors or the school food authorities were asked to distribute
all questionnaires to employees within their district.
A total of 3,850 surveys were mailed to selected school district
directors, who were asked to distribute surveys to employees.
The goal was to receive 500 complete and usable questionnaires
for data analysis, which would represent a 14% response
rate and would have been similar to previous research (12).
Approximately 550 (15%) surveys were mailed to each region.


https://nces.ed.gov/ccd/districtsearch/

Food Protection Trends - November/December 2020

Table of Contents for the Digital Edition of Food Protection Trends - November/December 2020

Characterizing Microbial Cross-Contamination on Large Surfaces Using a Traditional “Cloth and Bucket” Disinfection Method
Sanitation Monitoring of Stainless Steel Surfaces with a Test for Total Adenylates
Variability in the Microbial Profile of Retail Cricket Powders in the U.S. Retail Market
Exploring Food Truck Food Safety Training and Practices in the United States: A Qualitative Study
Use of the Theory of Planned Behavior to Determine Food Safety Behavioral Intentions among Child Nutrition Employees
Produce Safety Alliance Train-the-Trainer Course: Developing Trainers to Support Fruit and Vegetable Growers
Beyond the Bio Manpreet Singh
PDG Highlight Food Packaging Professional Development Group
General Interest Paper Exploring Novel Technologies to Enhance Food Safety Training and Research Opportunities
General Interest Paper Food Safety and Quality Data Management Using Artificial Intelligence
Industry Products
Coming Events
Food Protection Trends - November/December 2020 - Cover1
Food Protection Trends - November/December 2020 - Cover2
Food Protection Trends - November/December 2020 - 385
Food Protection Trends - November/December 2020 - 386
Food Protection Trends - November/December 2020 - 387
Food Protection Trends - November/December 2020 - 388
Food Protection Trends - November/December 2020 - 389
Food Protection Trends - November/December 2020 - 390
Food Protection Trends - November/December 2020 - 391
Food Protection Trends - November/December 2020 - Characterizing Microbial Cross-Contamination on Large Surfaces Using a Traditional “Cloth and Bucket” Disinfection Method
Food Protection Trends - November/December 2020 - 393
Food Protection Trends - November/December 2020 - 394
Food Protection Trends - November/December 2020 - 395
Food Protection Trends - November/December 2020 - 396
Food Protection Trends - November/December 2020 - 397
Food Protection Trends - November/December 2020 - 398
Food Protection Trends - November/December 2020 - 399
Food Protection Trends - November/December 2020 - 400
Food Protection Trends - November/December 2020 - 401
Food Protection Trends - November/December 2020 - Sanitation Monitoring of Stainless Steel Surfaces with a Test for Total Adenylates
Food Protection Trends - November/December 2020 - 403
Food Protection Trends - November/December 2020 - 404
Food Protection Trends - November/December 2020 - 405
Food Protection Trends - November/December 2020 - 406
Food Protection Trends - November/December 2020 - Variability in the Microbial Profile of Retail Cricket Powders in the U.S. Retail Market
Food Protection Trends - November/December 2020 - 408
Food Protection Trends - November/December 2020 - 409
Food Protection Trends - November/December 2020 - 410
Food Protection Trends - November/December 2020 - 411
Food Protection Trends - November/December 2020 - 412
Food Protection Trends - November/December 2020 - Exploring Food Truck Food Safety Training and Practices in the United States: A Qualitative Study
Food Protection Trends - November/December 2020 - 414
Food Protection Trends - November/December 2020 - 415
Food Protection Trends - November/December 2020 - 416
Food Protection Trends - November/December 2020 - 417
Food Protection Trends - November/December 2020 - 418
Food Protection Trends - November/December 2020 - 419
Food Protection Trends - November/December 2020 - 420
Food Protection Trends - November/December 2020 - 421
Food Protection Trends - November/December 2020 - 422
Food Protection Trends - November/December 2020 - 423
Food Protection Trends - November/December 2020 - Use of the Theory of Planned Behavior to Determine Food Safety Behavioral Intentions among Child Nutrition Employees
Food Protection Trends - November/December 2020 - 425
Food Protection Trends - November/December 2020 - 426
Food Protection Trends - November/December 2020 - 427
Food Protection Trends - November/December 2020 - 428
Food Protection Trends - November/December 2020 - 429
Food Protection Trends - November/December 2020 - 430
Food Protection Trends - November/December 2020 - 431
Food Protection Trends - November/December 2020 - 432
Food Protection Trends - November/December 2020 - 433
Food Protection Trends - November/December 2020 - 434
Food Protection Trends - November/December 2020 - Produce Safety Alliance Train-the-Trainer Course: Developing Trainers to Support Fruit and Vegetable Growers
Food Protection Trends - November/December 2020 - 436
Food Protection Trends - November/December 2020 - 437
Food Protection Trends - November/December 2020 - 438
Food Protection Trends - November/December 2020 - 439
Food Protection Trends - November/December 2020 - 440
Food Protection Trends - November/December 2020 - 441
Food Protection Trends - November/December 2020 - 442
Food Protection Trends - November/December 2020 - 443
Food Protection Trends - November/December 2020 - 444
Food Protection Trends - November/December 2020 - 445
Food Protection Trends - November/December 2020 - 446
Food Protection Trends - November/December 2020 - 447
Food Protection Trends - November/December 2020 - 448
Food Protection Trends - November/December 2020 - 449
Food Protection Trends - November/December 2020 - 450
Food Protection Trends - November/December 2020 - 451
Food Protection Trends - November/December 2020 - Beyond the Bio Manpreet Singh
Food Protection Trends - November/December 2020 - 453
Food Protection Trends - November/December 2020 - 454
Food Protection Trends - November/December 2020 - PDG Highlight Food Packaging Professional Development Group
Food Protection Trends - November/December 2020 - General Interest Paper Exploring Novel Technologies to Enhance Food Safety Training and Research Opportunities
Food Protection Trends - November/December 2020 - 457
Food Protection Trends - November/December 2020 - 458
Food Protection Trends - November/December 2020 - 459
Food Protection Trends - November/December 2020 - 460
Food Protection Trends - November/December 2020 - 461
Food Protection Trends - November/December 2020 - 462
Food Protection Trends - November/December 2020 - 463
Food Protection Trends - November/December 2020 - General Interest Paper Food Safety and Quality Data Management Using Artificial Intelligence
Food Protection Trends - November/December 2020 - 465
Food Protection Trends - November/December 2020 - 466
Food Protection Trends - November/December 2020 - 467
Food Protection Trends - November/December 2020 - Industry Products
Food Protection Trends - November/December 2020 - 469
Food Protection Trends - November/December 2020 - 470
Food Protection Trends - November/December 2020 - 471
Food Protection Trends - November/December 2020 - Coming Events
Food Protection Trends - November/December 2020 - Cover3
Food Protection Trends - November/December 2020 - Cover4
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