PHLT 8048 Week 10 Falsifiable Prediction Memo Example

Course	PHLT 8048 Research and Application of Public Health and Behavior Change Theories
Week	Week 10
Paper type	Prediction memo
Length	About 830 words, 5 pages
Format	Memo, APA 7 citations
School	Walden University
Program	PhD in Public Health
Updated	October 2026

Free sample paper for PHLT 8048 Week 10

Memorandum

To: Occupational Health and Safety Committee, composite regional hospital system

From: Employee Health Program Lead (DrPH student)

Date: Month Day, Year

Re: What our chosen theory predicts for next year's pilot, stated so that the pilot can prove it wrong

Why This Memo

Last month the program committed to the COM-B model as the framework for reducing drowsy driving among night-shift staff. A framework that can explain any result after the fact is of little use for planning, so this memo states, before next year's pilot begins, four predictions the COM-B diagnosis makes. Each is paired with the observation that would count against it and with what the theory of planned behavior, the framework we set aside, would expect. Popper (1959) argued that a theory's value lies in the observations it forbids; these predictions are the observations our choice forbids.

What this page is doingFraming the memo around what the theory forbids makes clear that the predictions are meant to be testable, not decorative.

Prediction 1: Capacity Changes Behavior Without Changing Beliefs

The pilot will expand the nap room on the medical tower from two beds to six. Because COM-B lets physical opportunity act on behavior without passing through beliefs (Michie et al., 2011), we predict that the share of badge-outs after a third consecutive night followed by a garage exit more than 60 minutes later, our marker of a nap, will climb by 10 percentage points or more within three months, with no meaningful change in staff attitudes toward drowsy driving measured by survey.

Counting against it: the nap share does not rise, or rises only among staff whose attitudes also shifted.

The rival's expectation: the theory of planned behavior would expect added capacity to matter only for staff who already intended to nap and were blocked; it would predict that attitudes and intentions measured at baseline explain who naps after the expansion better than capacity alone (Ajzen, 1991).

Prediction 2: Habit Predicts the Drive Home Better Than Intention

Among staff with at least a year of night shifts, we predict that habit strength, measured with a validated self-report habit index adapted to the drive home (Verplanken & Orbell, 2003), will predict driving straight home after third nights better than stated intention to nap, with habit explaining at least twice the variance that intention explains.

Counting against it: intention predicts the drive home as well as or better than habit.

The rival's expectation: intention should be the strongest predictor, with habit adding little once intention and perceived control are measured.

Prediction 3: A Default Ride Offer Outperforms an Equal Offer That Requires Asking

In two units, a ride voucher will be offered automatically at badge-out after third nights, with staff able to decline; in two matched units, the same voucher will be available on request. COM-B's attention to automatic processes implies that defaults change behavior by working with them rather than against them, as default options have done in other domains (Johnson & Goldstein, 2003). We predict that voucher use will be at least twice as high in default units.

Counting against it: use is similar in both arrangements.

The rival's expectation: because the voucher's value and availability are the same in both arrangements, perceived behavioral control and attitudes are similar, so the theory of planned behavior would expect little difference.

Prediction 4: An Awareness Campaign Moves Beliefs but Not the Drive Home

In the pilot's first month, before other changes, all night staff will see a campaign on drowsy driving risk. We predict that survey measures of perceived risk will rise but that the share of staff driving straight home after third nights will not change, since the COM-B diagnosis locates the barrier in opportunity and automatic motivation rather than in beliefs workers already hold.

Counting against it: driving straight home falls meaningfully after the campaign alone.

The rival's expectation: a campaign that strengthens beliefs about the risk should make attitudes less favorable, weaken intentions and reduce the behavior.

How the Predictions Will Be Judged

The four predictions will be checked against badge-out and garage exit records, surveys at baseline and three months, and voucher logs. If two or more fail, the program will revisit the choice of framework rather than explain the failures away. If Predictions 1, 3 and 4 hold but Prediction 2 fails, the diagnosis would be partly right: opportunity matters, but habit may matter less than assumed, and the planned behavior account's belief work would deserve a larger role.

What the Predictions Do Not Test

The pilot cannot test every claim of the diagnosis. It will not show whether training in recognizing sleepiness changes behavior on the road, since road behavior is not observed, and it will not settle whether limits on consecutive nights would reduce drowsy drives, since scheduling policy is outside the pilot's scope. Those claims remain untested and are named here so that a successful pilot is not read as confirming them.

Requests

I ask the committee to approve the nap room expansion and the voucher arrangement in four units, and to allow the program to collect garage exit times, linked to badge-out by study number, for the pilot's duration.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T

Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives? Science, 302(5649), 1338-1339. https://doi.org/10.1126/science.1091721

Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6, Article 42. https://doi.org/10.1186/1748-5908-6-42

Popper, K. (1959). The logic of scientific discovery. Hutchinson.

Verplanken, B., & Orbell, S. (2003). Reflections on past behavior: A self-report index of habit strength. Journal of Applied Social Psychology, 33(6), 1313-1330. https://doi.org/10.1111/j.1559-1816.2003.tb01951.x

The Walden PHLT 8048 Week 10 instructions

The tenth assignment usually asks students to state what their chosen theory predicts for their behavior and how those predictions could be shown wrong. Faculty expect a small number of specific predictions, each with the observation that would count against it, written before any data are collected. Including what the rejected theory would expect makes the memo a test of the selection rather than of the theory alone. Predictions should be measurable with data the program can actually obtain, and any thresholds should be stated plainly. A decision rule for what happens if predictions fail shows the commitment is real. Strong memos also say what the predictions do not test. The memo is addressed to the decision-makers who will run or approve the pilot, so it should be clear and concise.

Inside the PHLT 8048 Week 10 example

The memo explains why predictions are stated in advance, citing Popper on what a theory forbids. Four prediction sections follow, each with the prediction, the observation that would count against it and the rival's expectation: nap room expansion measured through badge-out and garage exit times, habit strength measured with a validated index against stated intention, default versus requested ride vouchers in matched units, and an awareness campaign measured by risk perception and behavior. A section explains how the predictions will be judged and commits the program to reconsider its framework if two or more fail. A section names the claims the pilot cannot test, and the memo closes with requests to the committee.

Where the marks sit in the PHLT 8048 Week 10 rubric

Graders reward predictions that are specific, measurable and genuinely risky. Predictions that any result would confirm, or that cannot be measured with available data, earn little. Credit goes to pairing each prediction with a disconfirming observation and the rival's expectation, to measures tied to objective records where possible, and to a decision rule that commits the program to act on failure. Naming what the pilot does not test prevents overinterpretation of success. Accurate citation of sources supporting each mechanism, such as habit measurement and default effects, is expected. The memo should be readable by a committee, with each prediction stated plainly. Consistency with the earlier diagnosis and selection defense is checked.

PHLT 8048 Week 10 help from the desk

Prediction memos lose force when predictions are vague enough to survive any result. For each prediction, name the measure, the threshold and what result would undercut it. Say what the theory you rejected would expect instead. Commit in writing to what you will do if predictions fail. Name the parts of your diagnosis the pilot cannot test. Keep each prediction to a sentence or two so the committee can hold you to it later. Prediction memos for other theory choices can follow this forbid-and-test structure.

Get a PHLT 8048 Week 10 example written to your instructions

Share the prompt, the rubric and the theory your section has selected, with a line on the behavior it targets. The PHLT 8048 Week 10 memo you get states falsifiable predictions, a disconfirming observation for each and the rival's expectation beside it. Allow 24 to 48 hours; the opening sample costs nothing.

More PHLT 8048 papers and PhD in Public Health samples

PHLT 8048 Week 10 questions, answered

Does each prediction need the rival's expectation?

It is what makes the memo about selection rather than about the theory alone. Where the two accounts predict the same thing, the memo says so, as it does for deliberate senders. Where they diverge, you have a prediction that could discriminate between them, and those are the ones a program team would most want to test.

Can a prediction memo be written without a program in place?

Yes. Predictions follow from the diagnosis, not from a running program, and the memo is addressed to a team that could design a test. The team, county and drivers in the sample are composites; a memo for your section would address whatever audience the prompt names. Addressing a real agency by name is unnecessary and usually unwise.

Where can I find a free PHLT 8048 Week 10 sample paper?

This page carries the complete Week 10 prediction memo, stating four COM-B predictions for a drowsy driving pilot with the observations that would count against each.

Why include thresholds in the predictions?

A threshold, such as a ten-point rise in napping, makes it clear in advance what counts as success, so the result cannot be reinterpreted afterward to fit the theory.

What happens if the predictions fail?

The memo commits the program to reconsider its framework if two or more predictions fail, rather than explaining the failures away after the fact.